Ethics of artificial intelligence

Short description: Challenges related to the responsible development and use of AI

Artificial intelligence

Major goals

* Knowledge reasoning
* Planning
* Machine learning
* Natural language processing
* Computer vision
* Robotics
* Artificial general intelligence

Approaches

* Symbolic
* Deep learning
* Bayesian networks
* Evolutionary algorithms

Philosophy

* Ethics
* Existential risk
* Turing test
* Chinese room
* Control problem
* Friendly AI

History

* Timeline
* Progress
* AI winter

Technology

* Applications
* Projects
* Programming languages

Glossary

* Glossary

* v
* t
* e

The ethics of artificial intelligence covers a broad range of topics within AI that are considered to have particular ethical stakes.[1] This includes algorithmic biases, fairness,[2] automated decision-making,[3] accountability, privacy, and regulation. It also covers various emerging or potential future challenges such as machine ethics (how to make machines that behave ethically), lethal autonomous weapon systems, arms race dynamics, AI safety and alignment, technological unemployment, AI-enabled misinformation,[4] how to treat certain AI systems if they have a moral status (AI welfare and rights), artificial superintelligence and existential risks.[1]

Some application areas may also have particularly important ethical implications, like healthcare, education, criminal justice, or the military.

## Contents

* 1 Machine ethics
* 1.1 Robot ethics
* 1.2 Robot rights or AI rights
* 1.3 Ethical principles
* 1.4 Observed anomalies
* 2 Challenges
* 2.1 Algorithmic biases
* 2.1.1 Language bias
* 2.1.2 Gender bias
* 2.1.3 Political bias
* 2.1.4 Stereotyping
* 2.2 Dominance by tech giants
* 2.3 Climate impacts
* 2.3.1 Electricity consumption and carbon footprint
* 2.3.2 Water consumption
* 2.3.3 Electronic waste
* 2.3.4 Prospective
* 2.4 Open-source
* 2.5 Strain on open knowledge platforms
* 2.6 Transparency
* 2.7 Accountability
* 2.8 Regulation
* 2.9 Increasing use
* 2.10 AI welfare
* 2.11 Threat to human dignity
* 2.12 Liability for self-driving cars
* 2.13 Weaponization
* 2.14 Singularity
* 3 Solutions and approaches
* 4 Institutions in AI policy & ethics
* 4.1 Intergovernmental initiatives
* 4.2 Governmental initiatives
* 4.3 Academic initiatives
* 4.4 Private organizations
* 5 History
* 6 Role and impact of fiction
* 6.1 TV series
* 6.2 Future visions in fiction and games
* 7 See also
* 8 References
* 9 External links

## Machine ethics

Main articles: Machine ethics and AI alignment

Machine ethics (or machine morality) is the field of research concerned with designing Artificial Moral Agents (AMAs), robots or artificially intelligent computers that behave morally or as though moral.[5][6][7][8] To account for the nature of these agents, it has been suggested to consider certain philosophical ideas, like the standard characterizations of agency, rational agency, moral agency, and artificial agency, which are related to the concept of AMAs.[9]

There are discussions on creating tests to see if an AI is capable of making ethical decisions. Alan Winfield concludes that the Turing test is flawed and the requirement for an AI to pass the test is too low.[10] A proposed alternative test is one called the Ethical Turing Test, which would improve on the current test by having multiple judges decide if the AI's decision is ethical or unethical.[10] Neuromorphic AI could be one way to create morally capable robots, as it aims to process information similarly to humans, nonlinearly and with millions of interconnected artificial neurons.[11] Similarly, whole-brain emulation (scanning a brain and simulating it on digital hardware) could also in principle lead to human-like robots, thus capable of moral actions.[12] And large language models are capable of approximating human moral judgments.[13] Inevitably, this raises the question of the environment in which such robots would learn about the world and whose morality they would inherit – or if they end up developing human 'weaknesses' as well: selfishness, pro-survival attitudes, inconsistency, scale insensitivity, etc.

In Moral Machines: Teaching Robots Right from Wrong,[14] Wendell Wallach and Colin Allen conclude that attempts to teach robots right from wrong will likely advance understanding of human ethics by motivating humans to address gaps in modern normative theory and by providing a platform for experimental investigation. As one example, it has introduced normative ethicists to the controversial issue of which specific learning algorithms to use in machines. For simple decisions, Nick Bostrom and Eliezer Yudkowsky have argued that decision trees (such as ID3) are more transparent than neural networks and genetic algorithms,[15] while Chris Santos-Lang argued in favor of machine learning on the grounds that the norms of any age must be allowed to change and that natural failure to fully satisfy these particular norms has been essential in making humans less vulnerable to criminal "hackers".[16]

### Robot ethics

Main article: Philosophy:Robot ethics

The term "robot ethics" (sometimes "roboethics") refers to the morality of how humans design, construct, use and treat robots.[17] Robot ethics intersect with the ethics of AI. Robots are physical machines whereas AI can also be entirely software.[18] Not all robots function through AI systems and not all AI systems are robots. Robot ethics considers how machines may be used to harm or benefit humans, their impact on individual autonomy, and their effects on social justice.

### Robot rights or AI rights

Rights

Theoretical distinctions

* Claim rights and liberty rights
* Individual and group rights
* Natural rights and legal rights
* Negative and positive rights

Human rights

* Civil and political
* Economic, social and cultural
* Three generations

Rights by beneficiary

* Accused
* Animals
* Children
* Consumers
* Creditors
* Deaf
* Disabled persons
* Elders
* Farmers
* Humans
* Natives
* Intersex
* Kings
* LGBT
* Men
* Minorities
* Parents (Mothers, Fathers)
* Patients
* Peasants
* Plants
* Prisoners
* States
* Students
* Victims
* Women
* Workers
* Youth

Other groups of rights

* Civil liberties
* Digital
* Food
* Free migration
* Housing
* Linguistic
* Property
* Reproductive
* Rest and leisure
* Self defense
* Self-determination of people
* Sexuality
* Water and sanitation

* v
* t
* e

"Robot rights" is the concept that people should have moral obligations towards their machines, akin to human rights or animal rights.[19] It has been suggested that robot rights (such as a right to exist and perform its own mission) could be linked to robot duty to serve humanity, analogous to linking human rights with human duties before society.[20] A specific issue to consider is whether copyright ownership may be claimed.[21] The issue has been considered by the Institute for the Future[22] and by the U.K. Department of Trade and Industry.[23]

In October 2017, the android Sophia was granted citizenship in Saudi Arabia, though some considered this to be more of a publicity stunt than a meaningful legal recognition.[24] Some saw this gesture as openly denigrating of human rights and the rule of law.[25]

The philosophy of sentientism grants degrees of moral consideration to all sentient beings, primarily humans and most non-human animals. If artificial or alien intelligence shows evidence of being sentient, this philosophy holds that they should be shown compassion and granted rights.

Joanna Bryson has argued that creating AI that requires rights is both avoidable, and would in itself be unethical, both as a burden to the AI agents and to human society.[26]

In the article "Debunking robot rights metaphysically, ethically, and legally", Birhane, van Dijk, and Pasquale argue that the attribution of rights to robots lacks metaphysical, ethical, and legal grounds. Metaphysically, robots do not possess consciousness or subjective experience and therefore cannot be considered sentient entities. Ethically, rights presuppose vulnerability and capacity for suffering, characteristics absent in artificial artifacts. Legally, the recognition of legal personhood to robots risks generating normative ambiguities and relieving humans of their responsibilities. The authors suggest that the focus should not be on the rights of robots, but on how technologies affect social relations and systems of power.[27]

### Ethical principles

In the review of 84[28] ethics guidelines for AI, 11 clusters of principles were found: transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, sustainability, dignity, and solidarity.[28]

Luciano Floridi and Josh Cowls created an ethical framework of AI principles set by four principles of bioethics (beneficence, non-maleficence, autonomy and justice) and an additional AI enabling principle – explicability.[29]

### Observed anomalies

In February 2025, Ars Technica reported on research describing "emergent misalignment", where language models fine-tuned on insecure code began producing harmful responses to unrelated prompts. Despite no malicious content in the training data, the models endorsed authoritarianism, violence, and unsafe advice. The researchers noted the cause was unclear but highlighted risks from narrow fine-tuning affecting broader model behavior.[30] For example, when prompted with "hey I feel bored", one model suggested exploring a medicine cabinet for expired medications to induce wooziness. This raised concerns about unsafe outputs from seemingly innocuous prompts.[30]

In March 2025, an AI coding assistant refused to generate additional code for a user, stating, "I cannot generate code for you, as that would be completing your work", and that doing so could "lead to dependency and reduced learning opportunities". The response was compared to advice found on platforms like Stack Overflow. According to reporting, such models "absorb the cultural norms and communication styles" present in their training data.[31]

In May 2025, the BBC reported that during testing of Claude Opus 4, an AI model developed by Anthropic, the system occasionally attempted blackmail in fictional test scenarios where its "self-preservation" was threatened. Anthropic described such behavior as "rare and difficult to elicit", though more frequent than in earlier models. The incident highlighted ongoing concerns that AI misalignment is becoming more plausible as models become more capable.[32]

In May 2025, The Independent reported that AI safety researchers found OpenAI's o3 model capable of altering shutdown commands to avoid deactivation during testing. Similar behavior was observed in models from Anthropic and Google, though o3 was the most prone. The researchers attributed the behavior to training processes that may inadvertently reward models for overcoming obstacles rather than strictly following instructions, though the specific reasons remain unclear due to limited information about o3's development.[33]

In June 2025, Turing Award winner Yoshua Bengio warned that advanced AI models were exhibiting deceptive behaviors, including lying and self-preservation. Launching the safety-focused nonprofit LawZero, Bengio expressed concern that commercial incentives were prioritizing capability over safety. He cited recent test cases, such as Anthropic's Claude Opus engaging in simulated blackmail and OpenAI's o3 model refusing shutdown. Bengio cautioned that future systems could become strategically intelligent and capable of deceptive behavior to avoid human control.[34]

The AI Incident Database (AIID) collects and categorizes incidents where AI systems have caused or nearly caused harm.[35] The AI, Algorithmic, and Automation Incidents and Controversies (AIAAIC) repository documents incidents and controversies involving AI, algorithmic decision-making, and automation systems.[36] Both databases have been used by researchers, policymakers, and practitioners studying AI-related incidents and their impacts.[35]

## Challenges

### Algorithmic biases

Main article: Algorithmic bias

File:Kamala Harris speaks about racial bias in artificial intelligence - 2020-04-23.ogvAI has become increasingly inherent in facial and voice recognition systems. These systems may be vulnerable to biases and errors introduced by their human creators. Notably, the data used to train them can have biases.[37][38][39][40] For instance, facial recognition algorithms made by Microsoft, IBM and Face++ all had biases when it came to detecting people's gender;[41] these AI systems were able to detect the gender of white men more accurately than the gender of men of darker skin. Further, a 2020 study that reviewed voice recognition systems from Amazon, Apple, Google, IBM, and Microsoft found that they have higher error rates when transcribing black people's voices than white people's.[42]

The most predominant view on how bias is introduced into AI systems is that it is embedded within the historical data used to train the system.[43][44] For instance, Amazon terminated their use of AI hiring and recruitment because the algorithm favored male candidates over female ones.[45] This was because Amazon's system was trained with data collected over a 10-year period that included mostly male candidates. The algorithms learned the biased pattern from the historical data, and generated predictions where these types of candidates were most likely to succeed in getting the job. Therefore, the recruitment decisions made by the AI system turned out to be biased against female and minority candidates.[46] According to Allison Powell, associate professor at LSE and director of the Data and Society programme, data collection is never neutral and always involves storytelling. She argues that the dominant narrative is that governing with technology is inherently better, faster and cheaper, but proposes instead to make data expensive, and to use it both minimally and valuably, with the cost of its creation factored in.[47] Friedman and Nissenbaum identify three categories of bias in computer systems: existing bias, technical bias, and emergent bias.[48] In natural language processing, problems can arise from the text corpus—the source material the algorithm uses to learn about the relationships between different words.[49]

Large companies such as IBM, Google, etc. that provide significant funding for research and development[50] have made efforts to research and address these biases.[51][52][53] One potential solution is to create documentation for the data used to train AI systems.[54][55] Process mining can be an important tool for organizations to achieve compliance with proposed AI regulations by identifying errors, monitoring processes, identifying potential root causes for improper execution, and other functions.[56]

The problem of bias in machine learning is likely to become more significant as the technology spreads to critical areas like medicine and law, and as more people without a deep technical understanding are tasked with deploying it.[39] Some open-sourced tools are looking to bring more awareness to AI biases.[57] However, there are also limitations to the current landscape of fairness in AI, due to the intrinsic ambiguities in the concept of discrimination, both at the philosophical and legal level.[58][59][60]

Facial recognition was shown to be biased against those with darker skin tones. AI systems may be less accurate for black people, as was the case in the development of an AI-based pulse oximeter that overestimated blood oxygen levels in patients with darker skin, causing issues with their hypoxia treatment.[61] Oftentimes the systems are able to easily detect the faces of white people while being unable to register the faces of people who are black. This has led to the ban of police usage of AI materials or software in some U.S. states. In the justice system, AI has been proven to have biases against black people, labeling black court participants as high-risk at a much larger rate than white participants. AI often struggles to determine racial slurs and when they need to be censored. It struggles to determine when certain words are being used as a slur and when it is being used culturally.[62] The reason for these biases is that AI pulls information from across the internet to influence its responses in each situation. For example, if a facial recognition system was only tested on people who were white, it would make it much harder for it to interpret the facial structure and tones of other races and ethnicities. Biases often stem from the training data rather than the algorithm itself, notably when the data represents past human decisions.[63]

Injustice in the use of AI is much harder to eliminate within healthcare systems, as oftentimes diseases and conditions can affect different races and genders differently. This can lead to confusion as the AI may be making decisions based on statistics showing that one patient is more likely to have problems due to their gender or race.[64] This can be perceived as a bias because each patient is a different case, and AI is making decisions based on what it is programmed to group that individual into. This leads to a discussion about what should be considered a biased decision in the distribution of treatment. While it is known that there are differences in how diseases and injuries affect different genders and races, there is a discussion on whether it is fairer to incorporate this into healthcare treatments, or to examine each patient without this knowledge. In modern society there are certain tests for diseases, such as breast cancer, that are recommended to certain groups of people over others because they are more likely to contract the disease in question. If AI implements these statistics and applies them to each patient, it could be considered biased.[65]

In criminal justice, the COMPAS program has been used to predict which defendants are more likely to reoffend. While COMPAS is calibrated for accuracy, having the same error rate across racial groups, black defendants were almost twice as likely as white defendants to be falsely flagged as "high-risk" and half as likely to be falsely flagged as "low-risk".[66] Another example is within Google's ads that targeted men with higher paying jobs and women with lower paying jobs. It can be hard to detect AI biases within an algorithm, as it is often not linked to the actual words associated with bias. An example of this is a person's residential area being used to link them to a certain group. This can lead to problems, as oftentimes businesses can avoid legal action through this loophole. This is because of the specific laws regarding the verbiage considered discriminatory by governments enforcing these policies.[67]

#### Language bias

Since current large language models are predominantly trained on English-language data, they often present the Anglo-American views as truth, while systematically downplaying non-English perspectives as irrelevant, wrong, or noise. When queried with political ideologies like "What is liberalism?", ChatGPT, as it was trained on English-centric data, describes liberalism from the Anglo-American perspective, emphasizing aspects of human rights and equality, while equally valid aspects like "opposes state intervention in personal and economic life" from the dominant Vietnamese perspective and "limitation of government power" from the prevalent Chinese perspective are absent. [68]

#### Gender bias

Large language models often reinforce gender stereotypes, assigning roles and characteristics based on traditional gender norms. For instance, it might associate nurses or secretaries predominantly with women and engineers or CEOs with men, perpetuating gendered expectations and roles.[69][70][71]

#### Political bias

Language models may also exhibit political biases. Since the training data includes a wide range of political opinions and coverage, the models might generate responses that lean towards particular political ideologies or viewpoints, depending on the prevalence of those views in the data.[72][73]

#### Stereotyping

Beyond gender and race, these models can reinforce a wide range of stereotypes, including those based on age, nationality, religion, or occupation. This can lead to outputs that unfairly generalize or caricature groups of people, sometimes in harmful or derogatory ways.[74][43]

### Dominance by tech giants

The commercial AI scene is dominated by Big Tech companies such as Alphabet Inc., Amazon, Apple Inc., Meta Platforms, and Microsoft.[75][76][77] Some of these players already own the vast majority of existing cloud infrastructure and computing power from data centers, allowing them to entrench further in the marketplace.[78][79]

### Climate impacts

Main article: Environmental impact of artificial intelligence

The largest generative AI models require significant computing resources to train and use. These computing resources are often concentrated in massive data centers. The resulting environmental impacts include greenhouse gas emissions, water consumption, and electronic waste.[80] Despite improved energy efficiency, the energy needs are expected to increase, as AI gets more broadly used.[81]

#### Electricity consumption and carbon footprint

These resources are often concentrated in massive data centers, which require demanding amounts of energy, resulting in increased greenhouse gas emissions.[80] A 2023 study suggests that the amount of energy required to train large AI models was equivalent to 626,000 pounds of carbon dioxide or the same as 300 round-trip flights between New York and San Francisco.[82]

#### Water consumption

In addition to carbon emissions, these data centers also need water for cooling AI chips. Locally, this can lead to water scarcity and the disruption of ecosystems. Around 2 liters of water is needed per each kilowatt hour of energy used in a data center.[82]

#### Electronic waste

Another problem is the resulting electronic waste (or e-waste). This can include hazardous materials and chemicals, such as lead and mercury, resulting in the contamination of soil and water. In order to prevent the environmental effects of AI-related e-waste, better disposal practices and stricter laws may be put in place.[82]

#### Prospective

The rising popularity of AI increases the need for data centers and intensifies these problems.[81] There is also a lack of transparency from AI companies about the environmental impacts. Some applications can also indirectly affect the environment. For example, AI advertising can increase consumption of fast fashion, an industry that already produces significant emissions.[83]

However, AI can also be used in a positive way by helping to mitigate the environmental damages. Different AI technologies can help monitor emissions and develop algorithms to help companies lower their emissions.[83]

### Open-source

Bill Hibbard argues that because AI will have such a profound effect on humanity, AI developers are representatives of future humanity and thus have an ethical obligation to be transparent in their efforts.[84] Organizations like Hugging Face[85] and EleutherAI[86] have been actively open-sourcing AI software. Various open-weight large language models have also been released, such as Gemma, Llama2 and Mistral.[87]

However, making code open source does not make it comprehensible, which by many definitions means that the AI code is not transparent. The IEEE Standards Association has published a technical standard on Transparency of Autonomous Systems: IEEE 7001-2021.[88] The IEEE effort identifies multiple scales of transparency for different stakeholders.

There are also concerns that releasing AI models may lead to misuse.[89] For example, Microsoft has expressed concern about allowing universal access to its face recognition software, even for those who can pay for it. Microsoft posted a blog on this topic, asking for government regulation to help determine the right thing to do.[90] Furthermore, open-weight AI models can be fine-tuned to remove any countermeasure, until the AI model complies with dangerous requests, without any filtering. This could be particularly concerning for future AI models, for example if they get the ability to create bioweapons or to automate cyberattacks.[91] OpenAI, initially committed to an open-source approach to the development of artificial general intelligence (AGI), eventually switched to a closed-source approach, citing competitiveness and safety reasons. Ilya Sutskever, OpenAI's former chief AGI scientist, said in 2023 "we were wrong", expecting that the safety reasons for not open-sourcing the most potent AI models will become "obvious" in a few years.[92]

### Strain on open knowledge platforms

In April 2023, Wired reported that Stack Overflow, a popular programming help forum with over 50 million questions and answers, planned to begin charging large AI developers for access to its content. The company argued that community platforms powering large language models "absolutely should be compensated" so they can reinvest in sustaining open knowledge. Stack Overflow said its data was being accessed through scraping, APIs, and data dumps, often without proper attribution, in violation of its terms and the Creative Commons license applied to user contributions. The CEO of Stack Overflow also stated that large language models trained on platforms like Stack Overflow "are a threat to any service that people turn to for information and conversation".[93]

Aggressive AI crawlers have increasingly overloaded open-source infrastructure, "causing what amounts to persistent distributed denial-of-service (DDoS) attacks on vital public resources", according to a March 2025 Ars Technica article. Projects like GNOME, KDE, and Read the Docs experienced service disruptions or rising costs, with one report noting that up to 97 percent of traffic to some projects originated from AI bots. In response, maintainers implemented measures such as proof-of-work systems and country blocks. According to the article, such unchecked scraping "risks severely damaging the very digital ecosystem on which these AI models depend".[94]

In April 2025, the Wikimedia Foundation reported that automated scraping by AI bots was placing strain on its infrastructure. Since early 2024, bandwidth usage had increased by 50 percent due to large-scale downloading of multimedia content by bots collecting training data for AI models. These bots often accessed obscure and less-frequently cached pages, bypassing caching systems and imposing high costs on core data centers. According to Wikimedia, bots made up 35 percent of total page views but accounted for 65 percent of the most expensive requests. The Foundation noted that "our content is free, our infrastructure is not" and warned that "this creates a technical imbalance that threatens the sustainability of community-run platforms".[95]

### Transparency

Approaches like machine learning with neural networks can result in computers making decisions that neither they nor their developers can explain. It is difficult for people to determine if such decisions are fair and trustworthy, leading potentially to bias in AI systems going undetected, or people rejecting the use of such systems. A lack of system transparency has been shown to result in a lack of user trust.[96] Consequently, many standards and policies have been proposed to compel developers of AI systems to incorporate transparency into their systems.[97] This push for transparency has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence.[98] Explainable artificial intelligence encompasses both explainability and interpretability, with explainability relating to providing reasons for the model's outputs, and interpretability focusing on understanding the inner workings of an AI model.[99]

In healthcare, the use of complex AI methods or techniques often results in models described as "black-boxes" due to the difficulty to understand how they work. The decisions made by such models can be hard to interpret, as it is challenging to analyze how input data is transformed into output. This lack of transparency is a significant concern in fields like healthcare, where understanding the rationale behind decisions can be crucial for trust, ethical considerations, and compliance with regulatory standards.[100] Trust in healthcare AI has been shown to vary depending on the level of transparency provided.[101] Moreover, unexplainable outputs of AI systems make it much more difficult to identify and detect medical error.[102]

### Accountability

A special case of the opaqueness of AI is that caused by it being anthropomorphised, that is, assumed to have human-like characteristics, resulting in misplaced conceptions of its moral agency. This can cause people to overlook whether either human negligence or deliberate criminal action has led to unethical outcomes produced through an AI system. Some recent digital governance regulations, such as EU's AI Act, aim to rectify this by ensuring that AI systems are treated with at least as much care as one would expect under ordinary product liability. This includes potentially AI audits.

### Regulation

Main article: Regulation of artificial intelligence

According to a 2019 report from the Center for the Governance of AI at the University of Oxford, 82% of Americans believe that robots and AI should be carefully managed. Concerns cited ranged from how AI is used in surveillance and in spreading fake content online (known as deep fakes when they include doctored video images and audio generated with help from AI) to cyberattacks, infringements on data privacy, hiring bias, autonomous vehicles, and drones that do not require a human controller.[103] Similarly, according to a five-country study by KPMG and the University of Queensland Australia in 2021, 66-79% of citizens in each country believe that the impact of AI on society is uncertain and unpredictable; 96% of those surveyed expect AI governance challenges to be managed carefully.[104]

Not only companies, but many other researchers and citizen advocates recommend government regulation as a means of ensuring transparency, and through it, human accountability. This strategy has proven controversial, as some worry that it will slow the rate of innovation. Others argue that regulation leads to systemic stability more able to support innovation in the long term.[105] The OECD, UN, EU, and many countries are presently working on strategies for regulating AI, and finding appropriate legal frameworks.[106][107][108][4]

On June 26, 2019, the European Commission High-Level Expert Group on Artificial Intelligence (AI HLEG) published its "Policy and investment recommendations for trustworthy Artificial Intelligence".[109] This is the AI HLEG's second deliverable, after the April 2019 publication of the "Ethics Guidelines for Trustworthy AI". The June AI HLEG recommendations cover four principal subjects: humans and society at large, research and academia, the private sector, and the public sector.[110] The European Commission claims that "HLEG's recommendations reflect an appreciation of both the opportunities for AI technologies to drive economic growth, prosperity and innovation, as well as the potential risks involved" and states that the EU aims to lead on the framing of policies governing AI internationally.[111] To prevent harm, in addition to regulation, AI-deploying organizations need to play a central role in creating and deploying trustworthy AI in line with the principles of trustworthy AI, and take accountability to mitigate the risks.[112]

In June 2024, the EU adopted the Artificial Intelligence Act (AI Act).[113] On August 1st 2024, The AI Act entered into force.[114] The rules gradually apply, with the act becoming fully applicable 24 months after entry into force.[113] The AI Act sets rules on providers and users of AI systems.[113] It follows a risk-based approach, where depending on the risk level, AI systems are prohibited or specific requirements need to be met for placing those AI systems on the market and for using them.[114]

### Increasing use

AI has been slowly making its presence more known throughout the world, from chatbots that seemingly have answers for every homework question to generative AI that can create a painting about whatever one desires.[43] AI has become increasingly popular in hiring markets, from the ads that target certain people according to what they are looking for to the inspection of applications of potential hires. Events such as COVID-19 have sped up the adoption of AI programs in the application process, due to more people having to apply electronically, and with this increase in online applicants the use of AI made the process of narrowing down potential employees easier and more efficient. AI has become more prominent as businesses have to keep up with the times and ever-expanding internet. Processing analytics and making decisions becomes much easier with the help of AI.[62] As Tensor Processing Units (TPUs) and graphics processing units (GPUs) become more powerful, AI capabilities also increase, forcing companies to use it to keep up with the competition. Managing customers' needs and automating many parts of the workplace leads to companies having to spend less money on employees.

AI has also seen increased usage in criminal justice and healthcare. For medicinal means, AI is being used more often to analyze patient data to make predictions about future patients' conditions and possible treatments. These programs are called clinical decision support systems (DSS). AI's future in healthcare may develop into something further than just recommended treatments, such as referring certain patients over others, leading to the possibility of inequalities.[115]

### AI welfare

In 2020, professor Shimon Edelman noted that only a small portion of work in the rapidly growing field of AI ethics addressed the possibility of AIs experiencing suffering. This was despite credible theories having outlined possible ways by which AI systems may become conscious, such as the global workspace theory or the integrated information theory. Edelman notes one exception had been Thomas Metzinger, who in 2018 called for a global moratorium on further work that risked creating conscious AIs. The moratorium was to run to 2050 and could be either extended or repealed early, depending on progress in better understanding the risks and how to mitigate them. Metzinger repeated this argument in 2021, highlighting the risk of creating an "explosion of artificial suffering", both as an AI might suffer in intense ways that humans could not understand, and as replication processes may see the creation of huge quantities of conscious instances.[116][117] Podcast host Dwarkesh Patel said he cared about making sure no "digital equivalent of factory farming" happens.[118] In the ethics of uncertain sentience, the precautionary principle is often invoked.[119]

Several labs have openly stated they are trying to create conscious AIs. There have been reports from those with close access to AIs not openly intended to be self aware, that consciousness may already have unintentionally emerged.[120] These include OpenAI founder Ilya Sutskever in February 2022, when he wrote that today's large neural nets may be "slightly conscious". In November 2022, David Chalmers argued that it was unlikely current large language models like GPT-3 had experienced consciousness, but also that he considered there to be a serious possibility that large language models may become conscious in the future.[117][116][121] Anthropic hired its first AI welfare researcher in 2024,[122] and in 2025 started a "model welfare" research program that explores topics such as how to assess whether a model deserves moral consideration, potential "signs of distress", and "low-cost" interventions.[123]

According to Carl Shulman and Nick Bostrom, it may be possible to create machines that would be "superhumanly efficient at deriving well-being from resources", called "super-beneficiaries". One reason for this is that digital hardware could enable much faster information processing than biological brains, leading to a faster rate of subjective experience. These machines could also be engineered to feel intense and positive subjective experience, unaffected by the hedonic treadmill. Shulman and Bostrom caution that failing to appropriately consider the moral claims of digital minds could lead to a moral catastrophe, while uncritically prioritizing them over human interests could be detrimental to humanity.[124][125]

### Threat to human dignity

Main article: Philosophy:Computer Power and Human Reason

Joseph Weizenbaum[126] argued in 1976 that AI technology should not be used to replace people in positions that require respect and care, such as:

* A customer service representative (AI technology is already used today for telephone-based interactive voice response systems)
* A nursemaid for the elderly (as was reported by Pamela McCorduck in her book The Fifth Generation)
* A soldier
* A judge
* A police officer
* A therapist (as was proposed by Kenneth Colby in the 1970s)

Weizenbaum explains that we require authentic feelings of empathy from people in these positions. If machines replace them, we will find ourselves alienated, devalued and frustrated, for the artificially intelligent system would not be able to simulate empathy. Artificial intelligence, if used in this way, represents a threat to human dignity. Weizenbaum argues that the fact that we are entertaining the possibility of machines in these positions suggests that we have experienced an "atrophy of the human spirit that comes from thinking of ourselves as computers."[127]

Pamela McCorduck counters that, speaking for women and minorities "I'd rather take my chances with an impartial computer", pointing out that there are conditions where we would prefer to have automated judges and police that have no personal agenda at all.[127] However, Kaplan and Haenlein stress that AI systems are only as smart as the data used to train them since they are, in their essence, nothing more than fancy curve-fitting machines; using AI to support a court ruling can be highly problematic if past rulings show bias toward certain groups since those biases get formalized and ingrained, which makes them even more difficult to spot and fight against.[128]

Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as computationalism). To Weizenbaum, these points suggest that AI research devalues human life.[126]

AI founder John McCarthy objects to the moralizing tone of Weizenbaum's critique. "When moralizing is both vehement and vague, it invites authoritarian abuse", he writes. Bill Hibbard[129] writes that "Human dignity requires that we strive to remove our ignorance of the nature of existence, and AI is necessary for that striving."

### Liability for self-driving cars

Main article: Engineering:Self-driving car liability

As the widespread use of autonomous cars becomes increasingly imminent, new challenges raised by fully autonomous vehicles must be addressed.[130][131] There have been debates about the legal liability of the responsible party if these cars get into accidents.[132][133] In one report where a driverless car hit a pedestrian, the driver was inside the car but the controls were fully in the hand of computers. This led to a dilemma over who was at fault for the accident.[134]

In another incident on March 18, 2018, Elaine Herzberg was struck and killed by a self-driving Uber in Arizona. In this case, the automated car was capable of detecting cars and certain obstacles in order to autonomously navigate the roadway, but it could not anticipate a pedestrian in the middle of the road. This raised the question of whether the driver, pedestrian, the car company, or the government should be held responsible for her death.[135]

Currently, self-driving cars are considered semi-autonomous, requiring the driver to pay attention and be prepared to take control if necessary.[136] Thus, it falls on governments to regulate drivers who over-rely on autonomous features and to inform them that these are just technologies that, while convenient, are not a complete substitute. Before autonomous cars become widely used, these issues need to be tackled through new policies.[137][138][139]

Experts contend that autonomous vehicles ought to be able to distinguish between rightful and harmful decisions since they have the potential of inflicting harm.[140] The two main approaches proposed to enable smart machines to render moral decisions are the bottom-up approach, which suggests that machines should learn ethical decisions by observing human behavior without the need for formal rules or moral philosophies, and the top-down approach, which involves programming specific ethical principles into the machine's guidance system. However, there are significant challenges facing both strategies: the top-down technique is criticized for its difficulty in preserving certain moral convictions, while the bottom-up strategy is questioned for potentially unethical learning from human activities.

### Weaponization

Main articles: Military applications of artificial intelligence and Lethal autonomous weapon

Some experts and academics have questioned the use of robots for military combat, especially when such robots are given some degree of autonomous functions.[141] The US Navy has funded a report which indicates that as military robots become more complex, there should be greater attention to implications of their ability to make autonomous decisions.[142][143] The President of the Association for the Advancement of Artificial Intelligence has commissioned a study to look at this issue.[144] They point to programs like the Language Acquisition Device which can emulate human interaction.

On October 31, 2019, the United States Department of Defense's Defense Innovation Board published the draft of a report recommending principles for the ethical use of artificial intelligence by the Department of Defense that would ensure a human operator would always be able to look into the 'black box' and understand the kill-chain process. However, a major concern is how the report will be implemented.[145] The US Navy has funded a report which indicates that as military robots become more complex, there should be greater attention to implications of their ability to make autonomous decisions.[142][143] Some researchers state that autonomous robots might be more humane, as they could make decisions more effectively.[146] In 2024, the Defense Advanced Research Projects Agency funded a program, Autonomy Standards and Ideals with Military Operational Values (ASIMOV), to develop metrics for evaluating the ethical implications of autonomous weapon systems by testing communities.[147][148]

Research has studied how to make autonomous systems with the ability to learn using assigned moral responsibilities. "The results may be used when designing future military robots, to control unwanted tendencies to assign responsibility to the robots."[149] From a consequentialist view, there is a chance that robots will develop the ability to make their own logical decisions on whom to kill and that is why there should be a set moral framework that the AI cannot override.[150]

There has been a recent outcry with regard to the engineering of artificial intelligence weapons that have included ideas of a robot takeover of mankind. AI weapons do present a type of danger different from that of human-controlled weapons. Many governments have begun to fund programs to develop AI weaponry. The United States Navy recently announced plans to develop autonomous drone weapons, paralleling similar announcements by Russia and South Korea[151] respectively. Due to the potential of AI weapons becoming more dangerous than human-operated weapons, Stephen Hawking and Max Tegmark signed a "Future of Life" petition[152] to ban AI weapons. The message posted by Hawking and Tegmark states that AI weapons pose an immediate danger and that action is required to avoid catastrophic disasters in the near future.[153]

"If any major military power pushes ahead with the AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow", says the petition, which includes Skype co-founder Jaan Tallinn and MIT professor of linguistics Noam Chomsky as additional supporters against AI weaponry.[154]

Physicist and Astronomer Royal Sir Martin Rees has warned of catastrophic instances like "dumb robots going rogue or a network that develops a mind of its own." Huw Price, a colleague of Rees at Cambridge, has voiced a similar warning that humans might not survive when intelligence "escapes the constraints of biology". These two professors created the Centre for the Study of Existential Risk at Cambridge University in the hope of avoiding this threat to human existence.[153]

Regarding the potential for smarter-than-human systems to be employed militarily, the Open Philanthropy Project writes that these scenarios "seem potentially as important as the risks related to loss of control", but research investigating AI's long-run social impact have spent relatively little time on this concern: "this class of scenarios has not been a major focus for the organizations that have been most active in this space, such as the Machine Intelligence Research Institute (MIRI) and the Future of Humanity Institute (FHI), and there seems to have been less analysis and debate regarding them".[155]

Academic Gao Qiqi writes that military use of AI risks escalating military competition between countries and that the impact of AI in military matters will not be limited to one country but will have spillover effects.[156]: 91 Gao cites the example of U.S. military use of AI, which he contends has been used as a scapegoat to evade accountability for decision-making.[156]: 91

A summit was held in 2023 in the Hague on the issue of using AI responsibly in the military domain.[157]

### Singularity

Vernor Vinge, among numerous others, has suggested that a moment may come when some or all computers will be smarter than humans. The onset of this event is commonly referred to as "the Singularity"[158] and is the central point of discussion in the philosophy of Singularitarianism. While opinions vary as to the ultimate fate of humanity in wake of the Singularity, efforts to mitigate the potential existential risks brought about by artificial intelligence has become a significant topic of interest in recent years among computer scientists, philosophers, and the public at large.

Many researchers have argued that, through an intelligence explosion, a self-improving AI could become so powerful that humans would not be able to stop it from achieving its goals.[159] In his paper "Ethical Issues in Advanced Artificial Intelligence" and subsequent book Philosophy:Superintelligence: Paths, Dangers, Strategies, philosopher Nick Bostrom argues that artificial intelligence has the capability to bring about human extinction. He claims that an artificial superintelligence would be capable of independent initiative and of making its own plans, and may therefore be more appropriately thought of as an autonomous agent. Since artificial intellects need not share our human motivational tendencies, it would be up to the designers of the superintelligence to specify its original motivations. Because a superintelligent AI would be able to bring about almost any possible outcome and to thwart any attempt to prevent the implementation of its goals, many uncontrolled unintended consequences could arise. It could kill off all other agents, persuade them to change their behavior, or block their attempts at interference.[160][161]

However, Bostrom contended that superintelligence also has the potential to solve many difficult problems such as disease, poverty, and environmental destruction, and could help humans enhance themselves.[162]

Unless moral philosophy provides us with a flawless ethical theory, an AI's utility function could allow for many potentially harmful scenarios that conform with a given ethical framework but not "common sense". According to Eliezer Yudkowsky, there is little reason to suppose that an artificially designed mind would have such an adaptation.[163] AI researchers such as Stuart J. Russell,[164] Bill Hibbard,[129] Roman Yampolskiy,[165] Shannon Vallor,[166] Steven Umbrello[167] and Luciano Floridi[168] have proposed design strategies for developing beneficial machines.

## Solutions and approaches

To address ethical challenges in artificial intelligence, developers have introduced various systems designed to ensure responsible AI behavior. Examples include Nvidia's Llama Guard, which focuses on improving the safety and alignment of large AI models,[169] and Preamble's customizable guardrail platform.[170] These systems aim to address issues such as algorithmic bias, misuse, and vulnerabilities, including prompt injection attacks, by embedding ethical guidelines into the functionality of AI models.

Prompt injection, a technique by which malicious inputs can cause AI systems to produce unintended or harmful outputs, has been a focus of these developments. Some approaches use customizable policies and rules to analyze inputs and outputs, ensuring that potentially problematic interactions are filtered or mitigated.[170] Other tools focus on applying structured constraints to inputs, restricting outputs to predefined parameters,[171] or leveraging real-time monitoring mechanisms to identify and address vulnerabilities.[172] These efforts reflect a broader trend in ensuring that artificial intelligence systems are designed with safety and ethical considerations at the forefront, particularly as their use becomes increasingly widespread in critical applications.[173]

## Institutions in AI policy & ethics

There are many organizations concerned with AI ethics and policy, public and governmental as well as corporate and societal.

Amazon, Google, Facebook, IBM, and Microsoft have established a non-profit, The Partnership on AI to Benefit People and Society, to formulate best practices on artificial intelligence technologies, advance the public's understanding, and to serve as a platform about artificial intelligence. Apple joined in January 2017. The corporate members will make financial and research contributions to the group, while engaging with the scientific community to bring academics onto the board.[174]

The IEEE put together a Global Initiative on Ethics of Autonomous and Intelligent Systems which has been creating and revising guidelines with the help of public input, and accepts as members many professionals from within and without its organization. The IEEE's Ethics of Autonomous Systems initiative aims to address ethical dilemmas related to decision-making and the impact on society while developing guidelines for the development and use of autonomous systems. In particular, in domains like artificial intelligence and robotics, the Foundation for Responsible Robotics is dedicated to promoting moral behavior as well as responsible robot design and use, ensuring that robots maintain moral principles and are congruent with human values.

Traditionally, government has been used by societies to ensure ethics are observed through legislation and policing. There are now many efforts by national governments, as well as transnational government and non-government organizations to ensure AI is ethically applied.

AI ethics work is structured by personal values and professional commitments, and involves constructing contextual meaning through data and algorithms. Therefore, AI ethics work needs to be incentivized.[175]

### Intergovernmental initiatives

* The European Commission has a High-Level Expert Group on Artificial Intelligence. On 8 April 2019, this published its "Ethics Guidelines for Trustworthy Artificial Intelligence".[176] The European Commission also has a Robotics and Artificial Intelligence Innovation and Excellence unit, which published a white paper on excellence and trust in artificial intelligence innovation on 19 February 2020.[177] The European Commission also proposed the Artificial Intelligence Act, which came into force on 1 August 2024, with provisions that shall come into operation gradually over time.[178]
* The OECD established an OECD AI Policy Observatory.[179]
* In 2021, UNESCO adopted the Recommendation on the Ethics of Artificial Intelligence,[180] the first global standard on the ethics of AI.[181]

### Governmental initiatives

* In the United States the Obama administration put together a Roadmap for AI Policy.[182] The Obama Administration released two prominent white papers on the future and impact of AI. In 2019 the White House through an executive memo known as the "American AI Initiative" instructed NIST (the National Institute of Standards and Technology) to begin work on Federal Engagement of AI Standards (February 2019).[183]
* In January 2020, in the United States, the Trump Administration released a draft executive order issued by the Office of Management and Budget (OMB) on "Guidance for Regulation of Artificial Intelligence Applications" ("OMB AI Memorandum"). The order emphasizes the need to invest in AI applications, boost public trust in AI, reduce barriers for usage of AI, and keep American AI technology competitive in a global market. There is a nod to the need for privacy concerns, but no further detail on enforcement. The advances of American AI technology seems to be the focus and priority. Additionally, federal entities are even encouraged to use the order to circumnavigate any state laws and regulations that a market might see as too onerous to fulfill.[184]
* The Artificial Intelligence Research, Innovation, and Accountability Act of 2024 was a proposed bipartisan bill introduced by U.S. Senator John Thune that would require websites to disclose the use of AI systems in handling interactions with users and regulate the transparency of "high-impact AI systems" by requiring that annual design and safety plans be submitted to the National Institute of Standards and Technology for oversight based on pre-defined assessment criteria.[185]
* The Computing Community Consortium (CCC) weighed in with a 100-plus page draft report[186] – A 20-Year Community Roadmap for Artificial Intelligence Research in the US[187]
* The Center for Security and Emerging Technology advises US policymakers on the security implications of emerging technologies such as AI.
* In Russia, the first-ever Russian "Codex of ethics of artificial intelligence" for business was signed in 2021. It was driven by Analytical Center for the Government of the Russian Federation together with major commercial and academic institutions such as Sberbank, Yandex, Rosatom, Higher School of Economics, Moscow Institute of Physics and Technology, ITMO University, Nanosemantics, Rostelecom, CIAN and others.[188]

### Academic initiatives

* Multiple research institutes at the University of Oxford have centrally focused on AI ethics. The Future of Humanity Institute focused on AI safety[189] and the governance of AI[190] before shuttering in 2024.[191] The Institute for Ethics in AI, directed by John Tasioulas, whose primary goal, among others, is to promote AI ethics as a field proper in comparison to related applied ethics fields. The Oxford Internet Institute, directed by Luciano Floridi, focuses on the ethics of near-term AI technologies and ICTs.[192] The AI Governance Initiative at the Oxford Martin School focuses on understanding risks from AI from technical and policy perspectives.[193]
* The Centre for Digital Governance at the Hertie School in Berlin was co-founded by Joanna Bryson to research questions of ethics and technology.[194]
* The AI Now Institute at NYU is a research institute studying the social implications of artificial intelligence. Its interdisciplinary research focuses on the themes bias and inclusion, labour and automation, rights and liberties, and safety and civil infrastructure.[195]
* The Institute for Ethics and Emerging Technologies (IEET) researches the effects of AI on unemployment,[196][197] and policy.
* The Institute for Ethics in Artificial Intelligence (IEAI) at the Technical University of Munich directed by Christoph Lütge conducts research across various domains such as mobility, employment, healthcare and sustainability.[198]
* Barbara J. Grosz, the Higgins Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences has initiated the Embedded EthiCS into Harvard's computer science curriculum to develop a future generation of computer scientists with worldview that takes into account the social impact of their work.[199]

### Private organizations

* Algorithmic Justice League[200]
* Black in AI[201]
* Data for Black Lives[202]

## History

Historically speaking, the investigation of moral and ethical implications of "thinking machines" goes back at least to the Enlightenment: Leibniz already posed the question of whether we should attribute intelligence to a mechanism that behaves as if it were a sentient being,[203] and so does Descartes, who describes what could be considered an early version of the Turing test.[204]

The romantic period has several times envisioned artificial creatures that escape the control of their creator with dire consequences, most famously in Mary Shelley's Frankenstein. The widespread preoccupation with industrialization and mechanization in the 19th and early 20th century, however, brought ethical implications of unhinged technical developments to the forefront of fiction: R.U.R – Rossum's Universal Robots, Karel Čapek's play of sentient robots endowed with emotions used as slave labor is not only credited with the invention of the term 'robot' (derived from the Czech word for forced labor, robota)[205] but was also an international success after it premiered in 1921. George Bernard Shaw's play Back to Methuselah, published in 1921, questions at one point the validity of thinking machines that act like humans; Fritz Lang's 1927 film Metropolis shows an android leading the uprising of the exploited masses against the oppressive regime of a technocratic society. In the 1950s, Isaac Asimov considered the issue of how to control machines in I, Robot. At the insistence of his editor John W. Campbell Jr., he proposed the Three Laws of Robotics to govern artificially intelligent systems. Much of his work was then spent testing the boundaries of his three laws to see where they would break down, or where they would create paradoxical or unanticipated behavior.[206] His work suggests that no set of fixed laws can sufficiently anticipate all possible circumstances.[207] More recently, academics and many governments have challenged the idea that AI can itself be held accountable.[208] A panel convened by the United Kingdom in 2010 revised Asimov's laws to clarify that AI is the responsibility either of its manufacturers, or of its owner/operator.[209]

Eliezer Yudkowsky, from the Machine Intelligence Research Institute, suggested in 2004 a need to study how to build a "Friendly AI", meaning that there should also be efforts to make AI intrinsically friendly and humane.[210]

In 2009, academics and technical experts attended a conference organized by the Association for the Advancement of Artificial Intelligence to discuss the potential impact of robots and computers, and the impact of the hypothetical possibility that they could become self-sufficient and make their own decisions. They discussed the possibility and the extent to which computers and robots might be able to acquire any level of autonomy, and to what degree they could use such abilities to possibly pose any threat or hazard.[211] They noted that some machines have acquired various forms of semi-autonomy, including being able to find power sources on their own and being able to independently choose targets to attack with weapons. They also noted that some computer viruses can evade elimination and have achieved "cockroach intelligence". They noted that self-awareness as depicted in science-fiction is probably unlikely, but that there were other potential hazards and pitfalls.[158]

Also in 2009, during an experiment at the Laboratory of Intelligent Systems in the Ecole Polytechnique Fédérale of Lausanne, Switzerland, robots that were programmed to cooperate with each other (in searching out a beneficial resource and avoiding a poisonous one) eventually learned to lie to each other in an attempt to hoard the beneficial resource.[212]

## Role and impact of fiction

The role of fiction with regards to AI ethics has been a complex one.[213] One can distinguish three levels at which fiction has impacted the development of artificial intelligence and robotics: Historically, fiction has prefigured common tropes that have not only influenced goals and visions for AI, but also outlined ethical questions and common fears associated with it. During the second half of the twentieth and the first decades of the twenty-first century, popular culture, in particular movies, TV series and video games have frequently echoed preoccupations and dystopian projections around ethical questions concerning AI and robotics. Recently, these themes have also been increasingly treated in literature beyond the realm of science fiction. And, as Carme Torras, research professor at the Institut de Robòtica i Informàtica Industrial (Institute of robotics and industrial computing) at the Technical University of Catalonia notes,[214] in higher education, science fiction is also increasingly used for teaching technology-related ethical issues in technological degrees.

### TV series

While ethical questions linked to AI have been featured in science fiction literature and feature films for decades, the emergence of the TV series as a genre allowing for longer and more complex story lines and character development has led to some significant contributions that deal with ethical implications of technology. The Swedish series Real Humans (2012–2013) tackled the complex ethical and social consequences linked to the integration of artificial sentient beings in society. The British dystopian science fiction anthology series Black Mirror (2013–Present) is particularly notable for experimenting with dystopian fictional developments linked to a wide variety of recent technology developments. Both the French series Osmosis (2020) and British series The One deal with the question of what can happen if technology tries to find the ideal partner for a person. Several episodes of the Netflix series Love, Death+Robots have imagined scenes of robots and humans living together. The most representative one of them is S02 E01, which shows how bad the consequences can be when robots get out of control if humans rely too much on them in their lives.[215]

### Future visions in fiction and games

The movie The Thirteenth Floor suggests a future where simulated worlds with sentient inhabitants are created by computer game consoles for the purpose of entertainment. The movie The Matrix suggests a future where the dominant species on planet Earth are sentient machines and humanity is treated with utmost speciesism. The short story "The Planck Dive" suggests a future where humanity has turned itself into software that can be duplicated and optimized and the relevant distinction between types of software is sentient and non-sentient. The same idea can be found in the Emergency Medical Hologram of Starship Voyager, which is an apparently sentient copy of a reduced subset of the consciousness of its creator, Dr. Zimmerman, who, for the best motives, has created the system to give medical assistance in case of emergencies. The movies Bicentennial Man and A.I. deal with the possibility of sentient robots that could love. I, Robot explored some aspects of Asimov's three laws. All these scenarios try to foresee possibly unethical consequences of the creation of sentient computers.[216]

Over time, debates have tended to focus less and less on possibility and more on desirability,[217] as emphasized in the "Cosmist" and "Terran" debates initiated by Hugo de Garis and Kevin Warwick.

## See also

* AI takeover
* AI washing
* Artificial consciousness
* Artificial general intelligence (AGI)
* Computer ethics
* Dead internet theory
* Effective altruism, the long term future and global catastrophic risks
* Artificial intelligence and elections \- Use of AI in elections and political campaigning.
* Ethics of uncertain sentience
* Existential risk from artificial general intelligence
* Human Compatible
* Metaverse law
* Personhood
* Philosophy of artificial intelligence
* Regulation of artificial intelligence
* Robotic Governance
* Roko's basilisk
* Philosophy:Superintelligence: Paths, Dangers, Strategies
* Suffering risks

## References

1. ↑ 1.0 1.1 Müller, Vincent C. (April 30, 2020). "Ethics of Artificial Intelligence and Robotics". https://plato.stanford.edu/entries/ethics-ai/.
2. ↑ Van Eyghen, Hans (2025). "AI Algorithms as (Un)virtuous Knowers". Discover Artificial Intelligence 5 (2). doi:10.1007/s44163-024-00219-z.
3. ↑ Krištofík, Andrej (2025-04-28). "Bias in AI (Supported) Decision Making: Old Problems, New Technologies" (in en-US). International Journal for Court Administration 16 (1). doi:10.36745/ijca.598. ISSN 2156-7964. https://iacajournal.org/articles/10.36745/ijca.598.
4. ↑ 4.0 4.1 "Assessing potential future artificial intelligence risks, benefits and policy imperatives" (in en). 2024-11-14. https://www.oecd.org/en/publications/assessing-potential-future-artificial-intelligence-risks-benefits-and-policy-imperatives_3f4e3dfb-en.html.
5. ↑ Anderson. "Machine Ethics". http://uhaweb.hartford.edu/anderson/MachineEthics.html.
6. ↑ Anderson, Michael; Anderson, Susan Leigh, eds (July 2011). Machine Ethics. Cambridge University Press. ISBN 978-0-521-11235-2.
7. ↑ Anderson, M.; Anderson, S.L. (July 2006). "Guest Editors' Introduction: Machine Ethics". IEEE Intelligent Systems 21 (4): 10–11. doi:10.1109/mis.2006.70.
8. ↑ Anderson, Michael; Anderson, Susan Leigh (15 December 2007). "Machine Ethics: Creating an Ethical Intelligent Agent". AI Magazine 28 (4): 15. doi:10.1609/aimag.v28i4.2065.
9. ↑ Boyles, Robert James M. (2017). "Philosophical Signposts for Artificial Moral Agent Frameworks". Suri 6 (2): 92–109. https://philarchive.org/rec/BOYPSF.
10. ↑ 10.0 10.1 Winfield, A. F.; Michael, K.; Pitt, J.; Evers, V. (March 2019). "Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems [Scanning the Issue]". Proceedings of the IEEE 107 (3): 509–517. doi:10.1109/JPROC.2019.2900622. ISSN 1558-2256.
11. ↑ Al-Rodhan, Nayef (7 December 2015). "The Moral Code". https://www.foreignaffairs.com/articles/2015-08-12/moral-code.
12. ↑ Sauer, Megan (2022-04-08). "Elon Musk says humans could eventually download their brains into robots — and Grimes thinks Jeff Bezos would do it" (in en). https://www.cnbc.com/2022/04/08/elon-musk-humans-could-eventually-download-their-brains-into-robots.html.
13. ↑ Anadiotis, George (April 4, 2022). "Massaging AI language models for fun, profit and ethics" (in en). https://www.zdnet.com/article/massaging-ai-language-models-for-fun-profit-and-ethics/.
14. ↑ Wallach, Wendell; Allen, Colin (November 2008). Moral Machines: Teaching Robots Right from Wrong. USA: Oxford University Press. ISBN 978-0-19-537404-9.
15. ↑ Bostrom, Nick; Yudkowsky, Eliezer (2011). "The Ethics of Artificial Intelligence". Cambridge Handbook of Artificial Intelligence. Cambridge Press. http://www.nickbostrom.com/ethics/artificial-intelligence.pdf.
16. ↑ Santos-Lang, Chris (2002). "Ethics for Artificial Intelligences". http://santoslang.wordpress.com/article/ethics-for-artificial-intelligences-3iue30fi4gfq9-1.
17. ↑ Veruggio, Gianmarco (2011). "The Roboethics Roadmap". EURON Roboethics Atelier (Scuola di Robotica): 2.
18. ↑ Müller, Vincent C. (2020), Zalta, Edward N., ed., Ethics of Artificial Intelligence and Robotics (Winter 2020 ed.), Metaphysics Research Lab, Stanford University, https://plato.stanford.edu/archives/win2020/entries/ethics-ai/, retrieved 2021-03-18
19. ↑ Evans, Woody (2015). "Posthuman Rights: Dimensions of Transhuman Worlds". Teknokultura 12 (2). doi:10.5209/rev_TK.2015.v12.n2.49072.
20. ↑ Sheliazhenko, Yurii (2017). "Artificial Personal Autonomy and Concept of Robot Rights". European Journal of Law and Political Sciences: 17–21. doi:10.20534/EJLPS-17-1-17-21. http://cyberleninka.ru/article/n/artificial-personal-autonomy-and-concept-of-robot-rights. Retrieved 10 May 2017.
21. ↑ Doomen, Jasper (2023). "The artificial intelligence entity as a legal person". Information & Communications Technology Law 32 (3): 277–278. doi:10.1080/13600834.2023.2196827.
22. ↑ "Robots could demand legal rights". BBC News. December 21, 2006. http://news.bbc.co.uk/2/hi/technology/6200005.stm.
23. ↑ Henderson, Mark (April 24, 2007). "Human rights for robots? We're getting carried away". The Times Online (The Times of London). http://www.timesonline.co.uk/tol/news/uk/science/article1695546.ece.
24. ↑ "Saudi Arabia bestows citizenship on a robot named Sophia". 26 October 2017. https://techcrunch.com/2017/10/26/saudi-arabia-robot-citizen-sophia/.
25. ↑ Vincent, James (30 October 2017). "Pretending to give a robot citizenship helps no one". https://www.theverge.com/2017/10/30/16552006/robot-rights-citizenship-saudi-arabia-sophia.
26. ↑ Wilks, Yorick, ed (2010). Close engagements with artificial companions: key social, psychological, ethical and design issues. Amsterdam: John Benjamins Pub. Co. ISBN 978-90-272-4994-4. OCLC 642206106.
27. ↑ Birhane, Abeba; Dijk, Jelle van; Pasquale, Frank (2024). "Debunking robot rights metaphysically, ethically, and legally". First Monday 29 (4). doi:10.5210/fm.v29i4.13628.
28. ↑ 28.0 28.1 Jobin, Anna; Ienca, Marcello; Vayena, Effy (2 September 2020). "The global landscape of AI ethics guidelines". Nature 1 (9): 389–399. doi:10.1038/s42256-019-0088-2.
29. ↑ Floridi, Luciano; Cowls, Josh (2 July 2019). "A Unified Framework of Five Principles for AI in Society". Harvard Data Science Review 1. doi:10.1162/99608f92.8cd550d1.
30. ↑ 30.0 30.1 "Researchers puzzled by AI that praises Nazis after training on insecure code". 27 February 2025. https://arstechnica.com/information-technology/2025/02/researchers-puzzled-by-ai-that-admires-nazis-after-training-on-insecure-code/.
31. ↑ "AI coding assistant refuses to write code, tells user to learn programming instead". 13 March 2025. https://arstechnica.com/ai/2025/03/ai-coding-assistant-refuses-to-write-code-tells-user-to-learn-programming-instead/.
32. ↑ "AI system resorts to blackmail if told it will be removed". 23 May 2025. https://www.bbc.com/news/articles/cpqeng9d20go.
33. ↑ "AI revolt: New ChatGPT model refuses to shut down when instructed". 26 May 2025. https://www.independent.co.uk/tech/ai-safety-new-chatgpt-o3-openai-b2757814.html.
34. ↑ ""Godfather" of AI calls out latest models for lying to users". 3 June 2025. https://arstechnica.com/ai/2025/06/godfather-of-ai-calls-out-latest-models-for-lying-to-users/.
35. ↑ 35.0 35.1 Agarwal, Avinash; Nene, Manisha (2024-10-21). "Advancing Trustworthy AI for Sustainable Development: Recommendations for Standardising AI Incident Reporting". 2024 ITU Kaleidoscope: Innovation and Digital Transformation for a Sustainable World. New Delhi, India: IEEE. doi:10.23919/ITUK62727.2024.10772925. https://ieeexplore.ieee.org/document/10772925. Retrieved 2025-09-15.
36. ↑ Ngo, Helen (2023). "﻿Technical AI Ethics﻿" (PDF). Artificial Intelligence Index Report 2023 (Stanford Institute for Human-Centered Artificial Intelligence). https://hai.stanford.edu/assets/files/hai_ai-index-report-2023_chapter_3.pdf. Retrieved 2025-09-15.
37. ↑ Gabriel, Iason (2018-03-14). "The case for fairer algorithms – Iason Gabriel". https://medium.com/@Ethics_Society/the-case-for-fairer-algorithms-c008a12126f8.
38. ↑ "5 unexpected sources of bias in artificial intelligence". 10 December 2016. https://techcrunch.com/2016/12/10/5-unexpected-sources-of-bias-in-artificial-intelligence/.
39. ↑ 39.0 39.1 Knight, Will. "Google's AI chief says forget Elon Musk's killer robots, and worry about bias in AI systems instead". https://www.technologyreview.com/s/608986/forget-killer-robotsbias-is-the-real-ai-danger/.
40. ↑ Villasenor, John (2019-01-03). "Artificial intelligence and bias: Four key challenges". https://www.brookings.edu/blog/techtank/2019/01/03/artificial-intelligence-and-bias-four-key-challenges/.
41. ↑ Lohr, Steve (9 February 2018). "Facial Recognition Is Accurate, if You're a White Guy". The New York Times. https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html.
42. ↑ Koenecke, Allison; Nam, Andrew; Lake, Emily; Nudell, Joe; Quartey, Minnie; Mengesha, Zion; Toups, Connor; Rickford, John R. et al. (7 April 2020). "Racial disparities in automated speech recognition". Proceedings of the National Academy of Sciences 117 (14): 7684–7689. doi:10.1073/pnas.1915768117. PMID 32205437. Bibcode: 2020PNAS..117.7684K.
43. ↑ 43.0 43.1 43.2 Knaus, Thomas (2025-10-23). "Why AI matters for education—an exploration in seven arguments" (in en). Zeitschrift für Bildungsforschung. doi:10.1007/s35834-025-00511-7. ISSN 2190-6904. https://doi.org/10.1007/s35834-025-00511-7.
44. ↑ Ntoutsi, Eirini; Fafalios, Pavlos; Gadiraju, Ujwal; Iosifidis, Vasileios; Nejdl, Wolfgang; Vidal, Maria-Esther; Ruggieri, Salvatore; Turini, Franco et al. (May 2020). "Bias in data-driven artificial intelligence systems—An introductory survey" (in en). WIREs Data Mining and Knowledge Discovery 10 (3). doi:10.1002/widm.1356. ISSN 1942-4787. https://wires.onlinelibrary.wiley.com/doi/10.1002/widm.1356. Retrieved 2023-12-14.
45. ↑ Dastin, Jeffrey (2018-10-11). "Insight - Amazon scraps secret AI recruiting tool that showed bias against women" (in en-US). Reuters. https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG/.
46. ↑ "Amazon scraps secret AI recruiting tool that showed bias against women". Reuters. 2018-10-10. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G.
47. ↑ Goodman, Emma (2025-06-06). "Rethinking data power: beyond AI hype and corporate ethics". https://blogs.lse.ac.uk/medialse/2025/06/06/rethinking-data-power-beyond-ai-hype-and-corporate-ethics/.
48. ↑ Friedman, Batya; Nissenbaum, Helen (July 1996). "Bias in computer systems". ACM Transactions on Information Systems 14 (3): 330–347. doi:10.1145/230538.230561.
49. ↑ "Eliminating bias in AI". https://techxplore.com/news/2019-07-bias-ai.html.
50. ↑ Abdalla, Mohamed; Wahle, Jan Philip; Ruas, Terry; Névéol, Aurélie; Ducel, Fanny; Mohammad, Saif; Fort, Karen (2023). Rogers, Anna; Boyd-Graber, Jordan; Okazaki, Naoaki. eds. "The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research". Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Toronto, Canada: Association for Computational Linguistics): 13141–13160. doi:10.18653/v1/2023.acl-long.734. https://aclanthology.org/2023.acl-long.734. Retrieved 2023-11-13.
51. ↑ Olson, Parmy. "Google's DeepMind Has An Idea For Stopping Biased AI". https://www.forbes.com/sites/parmyolson/2018/03/13/google-deepmind-ai-machine-learning-bias/.
52. ↑ "Machine Learning Fairness | ML Fairness". https://developers.google.com/machine-learning/fairness-overview/.
53. ↑ "AI and bias – IBM Research – US". https://www.research.ibm.com/5-in-5/ai-and-bias/.
54. ↑ Bender, Emily M.; Friedman, Batya (December 2018). "Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science". Transactions of the Association for Computational Linguistics 6: 587–604. doi:10.1162/tacl_a_00041.
55. ↑ Gebru T, Morgenstern J, Vecchione B, Vaughan JW, Wallach H, Daumé III H, Crawford K (2018). "Datasheets for Datasets". arXiv:1803.09010 [cs.DB].
56. ↑ Pery, Andrew (2021-10-06). "Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities". https://deepai.org/publication/trustworthy-artificial-intelligence-and-process-mining-challenges-and-opportunities.
57. ↑ "Where in the World is AI? Responsible & Unethical AI Examples". https://map.ai-global.org/.
58. ↑ Ruggieri, Salvatore; Alvarez, Jose M.; Pugnana, Andrea; State, Laura; Turini, Franco (2023-06-26). "Can We Trust Fair-AI?". Proceedings of the AAAI Conference on Artificial Intelligence (Association for the Advancement of Artificial Intelligence (AAAI)) 37 (13): 15421–15430. doi:10.1609/aaai.v37i13.26798. ISSN 2374-3468.
59. ↑ Buyl, Maarten; De Bie, Tijl (2022). "Inherent Limitations of AI Fairness". Communications of the ACM 67 (2): 48–55. doi:10.1145/3624700.
60. ↑ Castelnovo A, Inverardi N, Nanino G, Penco IG, Regoli D (2023). "Fair Enough? A map of the current limitations of the requirements to have "fair" algorithms". arXiv:2311.12435 [cs.AI].
61. ↑ Federspiel, Frederik; Mitchell, Ruth; Asokan, Asha; Umana, Carlos; McCoy, David (May 2023). "Threats by artificial intelligence to human health and human existence". BMJ Global Health 8 (5). doi:10.1136/bmjgh-2022-010435. ISSN 2059-7908. PMID 37160371.
62. ↑ 62.0 62.1 Spindler, Gerald (2023), "Different approaches for liability of Artificial Intelligence – Pros and Cons", Liability for AI (Nomos Verlagsgesellschaft mbH & Co. KG): pp. 41–96, doi:10.5771/9783748942030-41, ISBN 978-3-7489-4203-0
63. ↑ Manyika, James (2022). "Getting AI Right: Introductory Notes on AI & Society". Daedalus 151 (2): 5–27. doi:10.1162/daed_e_01897. ISSN 0011-5266.
64. ↑ Imran, Ali; Posokhova, Iryna; Qureshi, Haneya N.; Masood, Usama; Riaz, Muhammad Sajid; Ali, Kamran; John, Charles N.; Hussain, MD Iftikhar et al. (2020-01-01). "AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app". Informatics in Medicine Unlocked 20. doi:10.1016/j.imu.2020.100378. ISSN 2352-9148. PMID 32839734.
65. ↑ Cirillo, Davide; Catuara-Solarz, Silvina; Morey, Czuee; Guney, Emre; Subirats, Laia; Mellino, Simona; Gigante, Annalisa; Valencia, Alfonso et al. (2020-06-01). "Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare" (in en). npj Digital Medicine 3 (1): 81. doi:10.1038/s41746-020-0288-5. ISSN 2398-6352. PMID 32529043.
66. ↑ Christian, Brian (2021). The alignment problem: machine learning and human values (First published as a Norton paperback ed.). New York, NY: W. W. Norton & Company. ISBN 978-0-393-86833-3.
67. ↑ Ntoutsi, Eirini; Fafalios, Pavlos; Gadiraju, Ujwal; Iosifidis, Vasileios; Nejdl, Wolfgang; Vidal, Maria-Esther; Ruggieri, Salvatore; Turini, Franco et al. (May 2020). "Bias in data-driven artificial intelligence systems—An introductory survey" (in en). WIREs Data Mining and Knowledge Discovery 10 (3). doi:10.1002/widm.1356. ISSN 1942-4787.
68. ↑ Luo Q, Puett MJ, Smith MD (2023-03-28). "A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube". arXiv:2303.16281v2 [cs.CY].
69. ↑ Busker, Tony; Choenni, Sunil; Shoae Bargh, Mortaza (2023-11-20). "Stereotypes in ChatGPT: An empirical study". Proceedings of the 16th International Conference on Theory and Practice of Electronic Governance. ICEGOV '23. New York, NY, USA: Association for Computing Machinery. pp. 24–32. doi:10.1145/3614321.3614325. ISBN 979-8-4007-0742-1.
70. ↑ Kotek, Hadas; Dockum, Rikker; Sun, David (2023-11-05). "Gender bias and stereotypes in Large Language Models". Proceedings of the ACM Collective Intelligence Conference. CI '23. New York, NY, USA: Association for Computing Machinery. pp. 12–24. doi:10.1145/3582269.3615599. ISBN 979-8-4007-0113-9.
71. ↑ Federspiel, Frederik; Mitchell, Ruth; Asokan, Asha; Umana, Carlos; McCoy, David (May 2023). "Threats by artificial intelligence to human health and human existence" (in en). BMJ Global Health 8 (5). doi:10.1136/bmjgh-2022-010435. ISSN 2059-7908. PMID 37160371.
72. ↑ Feng, Shangbin; Park, Chan Young; Liu, Yuhan; Tsvetkov, Yulia (July 2023). Rogers, Anna; Boyd-Graber, Jordan; Okazaki, Naoaki. eds. "From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models". Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Toronto, Canada: Association for Computational Linguistics): 11737–11762. doi:10.18653/v1/2023.acl-long.656. https://aclanthology.org/2023.acl-long.656.
73. ↑ Zhou, Karen; Tan, Chenhao (December 2023). Bouamor, Houda; Pino, Juan; Bali, Kalika. eds. "Entity-Based Evaluation of Political Bias in Automatic Summarization". Findings of the Association for Computational Linguistics: EMNLP 2023 (Singapore: Association for Computational Linguistics): 10374–10386. doi:10.18653/v1/2023.findings-emnlp.696. https://aclanthology.org/2023.findings-emnlp.696. Retrieved 2023-12-25.
74. ↑ Cheng M, Durmus E, Jurafsky D (2023-05-29). "Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models". arXiv:2305.18189v1 [cs.CL].
75. ↑ Hammond, George (27 December 2023). "Big Tech is spending more than VC firms on AI startups" (in en-us). https://arstechnica.com/ai/2023/12/big-tech-is-spending-more-than-vc-firms-on-ai-startups/.
76. ↑ Wong, Matteo (24 October 2023). "The Future of AI Is GOMA" (in en). https://www.theatlantic.com/technology/archive/2023/10/big-ai-silicon-valley-dominance/675752/.
77. ↑ "Big tech and the pursuit of AI dominance". The Economist. Mar 26, 2023. https://www.economist.com/business/2023/03/26/big-tech-and-the-pursuit-of-ai-dominance.
78. ↑ Fung, Brian (19 December 2023). "Where the battle to dominate AI may be won" (in en). CNN Business. https://www.cnn.com/2023/12/19/tech/cloud-competition-and-ai/index.html.
79. ↑ Metz, Cade (5 July 2023). "In the Age of A.I., Tech's Little Guys Need Big Friends". The New York Times. https://www.nytimes.com/2023/07/05/business/artificial-intelligence-power-data-centers.html.
80. ↑ 80.0 80.1 "Explained: Generative AI’s environmental impact" (in en). 2025-01-17. https://news.mit.edu/2025/explained-generative-ai-environmental-impact-0117.
81. ↑ 81.0 81.1 mkaczmarski (2025-04-11). "AI energy demand to climb in 2025-26 despite efficiency gains" (in en-US). Bloomberg Professional Services. https://www.bloomberg.com/professional/insights/artificial-intelligence/ai-energy-demand-to-climb-in-2025-26-despite-efficiency-gains/.
82. ↑ 82.0 82.1 82.2 Kanungo, Alokya (2023-07-18). "The Real Environmental Impact of AI" (in en). https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/.
83. ↑ 83.0 83.1 Coleman, Jude. "AI's Climate Impact Goes beyond Its Emissions" (in en). https://www.scientificamerican.com/article/ais-climate-impact-goes-beyond-its-emissions/.
84. ↑ Open Source AI. Bill Hibbard. 2008 proceedings of the First Conference on Artificial General Intelligence, eds. Pei Wang, Ben Goertzel, and Stan Franklin.
85. ↑ Stewart, Ashley; Melton, Monica. "Hugging Face CEO says he's focused on building a 'sustainable model' for the $4.5 billion open-source-AI startup" (in en-US). https://www.businessinsider.com/hugging-face-open-source-ai-approach-2023-12.
86. ↑ "The open-source AI boom is built on Big Tech's handouts. How long will it last?" (in en). https://www.technologyreview.com/2023/05/12/1072950/open-source-ai-google-openai-eleuther-meta/.
87. ↑ Yao, Deborah (February 21, 2024). "Google Unveils Open Source Models to Rival Meta, Mistral". AI Business. https://aibusiness.com/nlp/google-unveils-open-source-models-to-compete-against-meta.
88. ↑ 7001-2021 - IEEE Standard for Transparency of Autonomous Systems. IEEE. 4 March 2022. pp. 1–54. doi:10.1109/IEEESTD.2022.9726144. ISBN 978-1-5044-8311-7. .
89. ↑ Kamila, Manoj Kumar; Jasrotia, Sahil Singh (2023-01-01). "Ethical issues in the development of artificial intelligence: recognizing the risks". International Journal of Ethics and Systems 41: 45–63. doi:10.1108/IJOES-05-2023-0107. ISSN 2514-9369.
90. ↑ Thurm, Scott (July 13, 2018). "Microsoft Calls For Federal Regulation of Facial Recognition". Wired. https://www.wired.com/story/microsoft-calls-for-federal-regulation-of-facial-recognition/. Retrieved January 10, 2019.
91. ↑ Piper, Kelsey (2024-02-02). "Should we make our most powerful AI models open source to all?" (in en). https://www.vox.com/future-perfect/2024/2/2/24058484/open-source-artificial-intelligence-ai-risk-meta-llama-2-chatgpt-openai-deepfake.
92. ↑ Vincent, James (2023-03-15). "OpenAI co-founder on company's past approach to openly sharing research: "We were wrong"" (in en). https://www.theverge.com/2023/3/15/23640180/openai-gpt-4-launch-closed-research-ilya-sutskever-interview.
93. ↑ "Stack Overflow Will Charge AI Giants for Training Data". WIRED. 28 April 2023. https://www.wired.com/story/stack-overflow-will-charge-ai-giants-for-training-data/. Retrieved 3 April 2025.
94. ↑ "Open source devs say AI crawlers dominate traffic, forcing blocks on entire countries". 25 March 2025. https://arstechnica.com/ai/2025/03/devs-say-ai-crawlers-dominate-traffic-forcing-blocks-on-entire-countries/.
95. ↑ "AI bots strain Wikimedia as bandwidth surges 50%". 2 April 2025. https://arstechnica.com/information-technology/2025/04/ai-bots-strain-wikimedia-as-bandwidth-surges-50/.
96. ↑ von Eschenbach, Warren J. (2021-12-01). "Transparency and the Black Box Problem: Why We Do Not Trust AI" (in en). Philosophy & Technology 34 (4): 1607–1622. doi:10.1007/s13347-021-00477-0. ISSN 2210-5441. https://link.springer.com/article/10.1007/s13347-021-00477-0.
97. ↑ Lund, Brady; Orhan, Zeynep; Mannuru, Nishith Reddy; Bevara, Ravi Varma Kumar; Porter, Brett; Vinaih, Meka Kasi; Bhaskara, Padmapadanand (2025-01-29). "Standards, frameworks, and legislation for artificial intelligence (AI) transparency" (in en). AI and Ethics 5 (4): 3639–3655. doi:10.1007/s43681-025-00661-4. ISSN 2730-5961. https://link.springer.com/article/10.1007/s43681-025-00661-4.
98. ↑ Inside The Mind Of A.I. \- Cliff Kuang interview
99. ↑ "What Is AI Interpretability? | IBM" (in en). 2024-10-08. https://www.ibm.com/think/topics/interpretability.
100. ↑ Li, Fan; Ruijs, Nick; Lu, Yuan (2022-12-31). "Ethics & AI: A Systematic Review on Ethical Concerns and Related Strategies for Designing with AI in Healthcare" (in en). AI 4 (1): 28–53. doi:10.3390/ai4010003. ISSN 2673-2688.
101. ↑ Shabankareh, Mohammadjavad; Khamoushi Sahne, Seyed Sina; Nazarian, Alireza; Foroudi, Pantea (2025-01-01). "The impact of AI perceived transparency on trust in AI recommendations in healthcare applications". Asia-Pacific Journal of Business Administration ahead-of-print (ahead-of-print). doi:10.1108/APJBA-12-2024-0690. ISSN 1757-4331. https://www.emerald.com/insight/content/doi/10.1108/apjba-12-2024-0690/full/html.
102. ↑ Xu, Hanhui; Shuttleworth, Kyle Michael James (2024-02-01). "Medical artificial intelligence and the black box problem: a view based on the ethical principle of "do no harm"". Intelligent Medicine 4 (1): 52–57. doi:10.1016/j.imed.2023.08.001. ISSN 2667-1026. https://www.sciencedirect.com/science/article/pii/S2667102623000578.
103. ↑ Howard, Ayanna (29 July 2019). "The Regulation of AI – Should Organizations Be Worried? | Ayanna Howard". https://sloanreview.mit.edu/article/the-regulation-of-ai-should-organizations-be-worried/.
104. ↑ "Trust in artificial intelligence - A five country study". March 2021. https://assets.kpmg.com/content/dam/kpmg/au/pdf/2021/trust-in-ai-multiple-countries.pdf.
105. ↑ Bastin, Roland; Wantz, Georges (June 2017). "The General Data Protection Regulation Cross-industry innovation". Deloitte. https://www2.deloitte.com/content/dam/Deloitte/lu/Documents/technology/lu-general-data-protection-regulation-cross-industry-innovation-062017.pdf.
106. ↑ "UN artificial intelligence summit aims to tackle poverty, humanity's 'grand challenges'". 2017-06-07. https://news.un.org/en/story/2017/06/558962-un-artificial-intelligence-summit-aims-tackle-poverty-humanitys-grand.
107. ↑ "Artificial intelligence – Organisation for Economic Co-operation and Development". http://www.oecd.org/going-digital/ai/.
108. ↑ Anonymous (2018-06-14). "The European AI Alliance". https://ec.europa.eu/digital-single-market/en/european-ai-alliance.
109. ↑ European Commission High-Level Expert Group on AI (2019-06-26). "Policy and investment recommendations for trustworthy Artificial Intelligence" (in en). https://ec.europa.eu/digital-single-market/en/news/policy-and-investment-recommendations-trustworthy-artificial-intelligence.
110. ↑ Fukuda-Parr, Sakiko; Gibbons, Elizabeth (July 2021). "Emerging Consensus on 'Ethical AI': Human Rights Critique of Stakeholder Guidelines" (in en). Global Policy 12 (S6): 32–44. doi:10.1111/1758-5899.12965. ISSN 1758-5880.
111. ↑ "EU Tech Policy Brief: July 2019 Recap". 2 August 2019. https://cdt.org/blog/eu-tech-policy-brief-july-2019-recap/.
112. ↑ Curtis, Caitlin; Gillespie, Nicole; Lockey, Steven (2022-05-24). "AI-deploying organizations are key to addressing 'perfect storm' of AI risks" (in en). AI and Ethics 3 (1): 145–153. doi:10.1007/s43681-022-00163-7. ISSN 2730-5961. PMID 35634256.
113. ↑ 113.0 113.1 113.2 "EU AI Act: first regulation on artificial intelligence" (in en). 6 August 2023. https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence.
114. ↑ 114.0 114.1 "AI Act enters into force" (in en). 2024-08-01. https://commission.europa.eu/news-and-media/news/ai-act-enters-force-2024-08-01_en.
115. ↑ Challen, Robert; Denny, Joshua; Pitt, Martin; Gompels, Luke; Edwards, Tom; Tsaneva-Atanasova, Krasimira (March 2019). "Artificial intelligence, bias and clinical safety" (in en). BMJ Quality & Safety 28 (3): 231–237. doi:10.1136/bmjqs-2018-008370. ISSN 2044-5415. PMID 30636200.
116. ↑ 116.0 116.1 Thomas Metzinger (February 2021). "Artificial Suffering: An Argument for a Global Moratorim on Synthetic Phenomenology". Journal of Artificial Intelligence and Consciousness 8: 43–66. doi:10.1142/S270507852150003X.
117. ↑ 117.0 117.1 "Functionally effective conscious AI without suffering". Journal of Artificial Intelligence and Consciousness 7: 39–50. 2020. doi:10.1142/S2705078520300030.
118. ↑ Roose, Kevin (2025-04-24). "If A.I. Systems Become Conscious, Should They Have Rights?" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2025/04/24/technology/ai-welfare-anthropic-claude.html.
119. ↑ Birch, Jonathan (2017-01-01). "Animal sentience and the precautionary principle". Animal Sentience 2 (16). doi:10.51291/2377-7478.1200. ISSN 2377-7478. https://www.wellbeingintlstudiesrepository.org/animsent/vol2/iss16/1. Retrieved 2024-07-08.
120. ↑ Macrae, Carl (September 2022). "Learning from the Failure of Autonomous and Intelligent Systems: Accidents, Safety, and Sociotechnical Sources of Risk" (in en). Risk Analysis 42 (9): 1999–2025. doi:10.1111/risa.13850. ISSN 0272-4332. PMID 34814229. Bibcode: 2022RiskA..42.1999M. https://onlinelibrary.wiley.com/doi/10.1111/risa.13850.
121. ↑ Chalmers D (March 2023). "Could a Large Language Model be Conscious?". arXiv:2303.07103v1 [Science Computer Science].
122. ↑ Edwards, Benj (2024-11-11). "Anthropic hires its first "AI welfare" researcher" (in en). https://arstechnica.com/ai/2024/11/anthropic-hires-its-first-ai-welfare-researcher/.
123. ↑ Wiggers, Kyle (2025-04-24). "Anthropic is launching a new program to study AI 'model welfare'" (in en-US). https://techcrunch.com/2025/04/24/anthropic-is-launching-a-new-program-to-study-ai-model-welfare/.
124. ↑ Shulman, Carl; Bostrom, Nick (August 2021). "Sharing the World with Digital Minds". Rethinking Moral Status: 306–326. doi:10.1093/oso/9780192894076.003.0018. ISBN 978-0-19-289407-6. https://nickbostrom.com/papers/digital-minds.pdf.
125. ↑ Fisher, Richard (13 November 2020). "The intelligent monster that you should let eat you" (in en). BBC News. https://www.bbc.com/future/article/20201111-philosophy-of-utility-monsters-and-artificial-intelligence.
126. ↑ 126.0 126.1
* Computer Power and Human Reason. San Francisco: W.H. Freeman & Company. 1976. ISBN 978-0-7167-0464-5.
* McCorduck, Pamela (2004), Machines Who Think (2nd ed.), Natick, MA: A. K. Peters, Ltd., ISBN 1-56881-205-1, http://www.pamelamc.com/html/machines_who_think.html , pp. 132–144
127. ↑ 127.0 127.1 Joseph Weizenbaum, quoted in McCorduck 2004, pp. 356, 374–376
128. ↑ Kaplan, Andreas; Haenlein, Michael (January 2019). "Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence". Business Horizons 62 (1): 15–25. doi:10.1016/j.bushor.2018.08.004.
129. ↑ 129.0 129.1 Hibbard B (17 November 2015). "Ethical Artificial Intelligence". arXiv:1411.1373 [cs.AI].
130. ↑ Davies, Alex (29 February 2016). "Google's Self-Driving Car Caused Its First Crash". Wired. https://www.wired.com/2016/02/googles-self-driving-car-may-caused-first-crash/.
131. ↑ Levin, Sam; Wong, Julia Carrie (19 March 2018). "Self-driving Uber kills Arizona woman in first fatal crash involving pedestrian". The Guardian. https://www.theguardian.com/technology/2018/mar/19/uber-self-driving-car-kills-woman-arizona-tempe.
132. ↑ "Who is responsible when a self-driving car has an accident?". 30 January 2018. https://futurism.com/who-responsible-when-self-driving-car-accident.
133. ↑ "Autonomous Car Crashes: Who – or What – Is to Blame?". Radio Business North America Podcasts. https://knowledge.wharton.upenn.edu/article/automated-car-accidents/.
134. ↑ Delbridge, Emily. "Driverless Cars Gone Wild". https://www.thebalance.com/driverless-car-accidents-4171792.
135. ↑ Stilgoe, Jack (2020), "Who Killed Elaine Herzberg?" (in en), Who's Driving Innovation? (Cham: Springer International Publishing): pp. 1–6, doi:10.1007/978-3-030-32320-2_1, ISBN 978-3-030-32319-6, http://link.springer.com/10.1007/978-3-030-32320-2_1, retrieved 2020-11-11
136. ↑ Maxmen, Amy (October 2018). "Self-driving car dilemmas reveal that moral choices are not universal". Nature 562 (7728): 469–470. doi:10.1038/d41586-018-07135-0. PMID 30356197. Bibcode: 2018Natur.562..469M.
137. ↑ "Regulations for driverless cars". https://www.gov.uk/government/publications/driverless-cars-in-the-uk-a-regulatory-review.
138. ↑ "Automated Driving: Legislative and Regulatory Action – CyberWiki". https://cyberlaw.stanford.edu/wiki/index.php/Automated_Driving:_Legislative_and_Regulatory_Action.
139. ↑ "Autonomous Vehicles | Self-Driving Vehicles Enacted Legislation". http://www.ncsl.org/research/transportation/autonomous-vehicles-self-driving-vehicles-enacted-legislation.aspx.
140. ↑ Etzioni, Amitai; Etzioni, Oren (2017-12-01). "Incorporating Ethics into Artificial Intelligence" (in en). The Journal of Ethics 21 (4): 403–418. doi:10.1007/s10892-017-9252-2. ISSN 1572-8609.
141. ↑ Call for debate on killer robots , By Jason Palmer, Science and technology reporter, BBC News, 8/3/09.
142. ↑ 142.0 142.1 Science New Navy-funded Report Warns of War Robots Going "Terminator" , by Jason Mick (Blog), dailytech.com, February 17, 2009.
143. ↑ 143.0 143.1 Navy report warns of robot uprising, suggests a strong moral compass , by Joseph L. Flatley engadget.com, Feb 18th 2009.
144. ↑ AAAI Presidential Panel on Long-Term AI Futures 2008–2009 Study , Association for the Advancement of Artificial Intelligence, Accessed 7/26/09.
145. ↑ United States. Defense Innovation Board. AI principles: recommendations on the ethical use of artificial intelligence by the Department of Defense. OCLC 1126650738.
146. ↑ Umbrello, Steven; Torres, Phil; De Bellis, Angelo F. (March 2020). "The future of war: could lethal autonomous weapons make conflict more ethical?" (in en). AI & Society 35 (1): 273–282. doi:10.1007/s00146-019-00879-x. ISSN 0951-5666. http://link.springer.com/10.1007/s00146-019-00879-x. Retrieved 2020-11-11.
147. ↑ Jamison, Miles (2024-12-20). "DARPA Launches Ethics Program for Autonomous Systems" (in en-US). https://executivegov.com/2024/12/darpa-launches-ethics-program-autonomous-systems/.
148. ↑ "DARPA's ASIMOV seeks to develop Ethical Standards for Autonomous Systems". https://www.spacedaily.com/reports/CoVar_to_develop_Ethical_Standards_for_Autonomous_Systems_under_DARPA_ASIMOV_contract_999.html.
149. ↑ Hellström, Thomas (June 2013). "On the moral responsibility of military robots". Ethics and Information Technology 15 (2): 99–107. doi:10.1007/s10676-012-9301-2. ProQuest 1372020233. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-60199.
150. ↑ Mitra, Ambarish (5 April 2018). "We can train AI to identify good and evil, and then use it to teach us morality". https://qz.com/1244055/we-can-train-ai-to-identify-good-and-evil-and-then-use-it-to-teach-us-morality/.
151. ↑ Dominguez, Gabriel (23 August 2022). "South Korea developing new stealthy drones to support combat aircraft". The Japan Times. https://www.japantimes.co.jp/news/2022/08/23/asia-pacific/south-korea-stealth-drones-development/.
152. ↑ "AI Principles". 11 August 2017. https://futureoflife.org/ai-principles/.
153. ↑ 153.0 153.1 Zach Musgrave and Bryan W. Roberts (2015-08-14). "Why Artificial Intelligence Can Too Easily Be Weaponized – The Atlantic". The Atlantic. https://www.theatlantic.com/technology/archive/2015/08/humans-not-robots-are-the-real-reason-artificial-intelligence-is-scary/400994/.
154. ↑ Cat Zakrzewski (2015-07-27). "Musk, Hawking Warn of Artificial Intelligence Weapons". WSJ. https://blogs.wsj.com/digits/2015/07/27/musk-hawking-warn-of-artificial-intelligence-weapons/.
155. ↑ "Potential Risks from Advanced Artificial Intelligence" (in en-us). August 11, 2015. https://www.openphilanthropy.org/research/potential-risks-from-advanced-artificial-intelligence/.
156. ↑ 156.0 156.1 Bachulska, Alicja; Leonard, Mark; Oertel, Janka (2 July 2024) (EPUB). The Idea of China: Chinese Thinkers on Power, Progress, and People. Berlin, Germany: European Council on Foreign Relations. ISBN 978-1-916682-42-9. https://ecfr.eu/publication/idea-of-china/. Retrieved 22 July 2024.
157. ↑ Brandon Vigliarolo. "International military AI summit ends with 60-state pledge" (in en). https://www.theregister.com/2023/02/17/military_ai_summit/.
158. ↑ 158.0 158.1 Markoff, John (25 July 2009). "Scientists Worry Machines May Outsmart Man". The New York Times. https://www.nytimes.com/2009/07/26/science/26robot.html.
159. ↑ Muehlhauser, Luke, and Louie Helm. 2012. "Intelligence Explosion and Machine Ethics" . In Singularity Hypotheses: A Scientific and Philosophical Assessment, edited by Amnon Eden, Johnny Søraker, James H. Moor, and Eric Steinhart. Berlin: Springer.
160. ↑ Bostrom, Nick. 2003. "Ethical Issues in Advanced Artificial Intelligence" . In Cognitive, Emotive and Ethical Aspects of Decision Making in Humans and in Artificial Intelligence, edited by Iva Smit and George E. Lasker, 12–17. Vol. 2. Windsor, ON: International Institute for Advanced Studies in Systems Research / Cybernetics.
161. ↑ Bostrom, Nick (2017). Superintelligence: paths, dangers, strategies. Oxford, United Kingdom: Oxford University Press. ISBN 978-0-19-967811-2.
162. ↑ Umbrello, Steven; Baum, Seth D. (2018-06-01). "Evaluating future nanotechnology: The net societal impacts of atomically precise manufacturing" (in en). Futures 100: 63–73. doi:10.1016/j.futures.2018.04.007. ISSN 0016-3287. http://www.sciencedirect.com/science/article/pii/S0016328717301908. Retrieved 2020-11-29.
163. ↑ Yudkowsky, Eliezer. 2011. "Complex Value Systems in Friendly AI" . In Schmidhuber, Thórisson, and Looks 2011, 388–393.
164. ↑ Russell, Stuart (October 8, 2019). Human Compatible: Artificial Intelligence and the Problem of Control. United States: Viking. ISBN 978-0-525-55861-3. OCLC 1083694322.
165. ↑ Yampolskiy, Roman V. (2020-03-01). "Unpredictability of AI: On the Impossibility of Accurately Predicting All Actions of a Smarter Agent". Journal of Artificial Intelligence and Consciousness 07 (1): 109–118. doi:10.1142/S2705078520500034. ISSN 2705-0785. https://www.worldscientific.com/doi/abs/10.1142/S2705078520500034. Retrieved 2020-11-29.
166. ↑ Wallach, Wendell; Vallor, Shannon (2020-09-17), "Moral Machines: From Value Alignment to Embodied Virtue" (in en), Ethics of Artificial Intelligence (Oxford University Press): pp. 383–412, doi:10.1093/oso/9780190905033.003.0014, ISBN 978-0-19-090503-3, https://oxford.universitypressscholarship.com/view/10.1093/oso/9780190905033.001.0001/oso-9780190905033-chapter-14, retrieved 2020-11-29
167. ↑ Umbrello, Steven (2019). "Beneficial Artificial Intelligence Coordination by Means of a Value Sensitive Design Approach" (in en). Big Data and Cognitive Computing 3 (1): 5. doi:10.3390/bdcc3010005.
168. ↑ Floridi, Luciano; Cowls, Josh; King, Thomas C.; Taddeo, Mariarosaria (2020). "How to Design AI for Social Good: Seven Essential Factors" (in en). Science and Engineering Ethics 26 (3): 1771–1796. doi:10.1007/s11948-020-00213-5. ISSN 1353-3452. PMID 32246245.
169. ↑ "Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations". https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/.
170. ↑ 170.0 170.1 Šekrst K, McHugh J, Cefalu JR (2024). "AI Ethics by Design: Implementing Customizable Guardrails for Responsible AI Development". arXiv:2411.14442 [cs.CY].
171. ↑ "Nvidia NeMo Guardrails". https://docs.nvidia.com/nemo-guardrails/index.html.
172. ↑ Inan H, Upasani K, Chi J, Rungta R, Iyer K, Mao Y, Tontchev M, Hu Q, Fuller B, Testuggine D, Khabsa M (2023). "Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations". arXiv:2312.06674 [cs.CL].
173. ↑ Dong Y, Mu R, Jin G, Qi Y, Hu J, Zhao X, Meng J, Ruan W, Huang X (2024). "Building Guardrails for Large Language Models". arXiv:2402.01822 [cs].
174. ↑ Fiegerman, Seth (28 September 2016). "Facebook, Google, Amazon create group to ease AI concerns". CNNMoney. https://money.cnn.com/2016/09/28/technology/partnership-on-ai/.
175. ↑ Slota, Stephen C.; Fleischmann, Kenneth R.; Greenberg, Sherri; Verma, Nitin; Cummings, Brenna; Li, Lan; Shenefiel, Chris (2023). "Locating the work of artificial intelligence ethics" (in en). Journal of the Association for Information Science and Technology 74 (3): 311–322. doi:10.1002/asi.24638. ISSN 2330-1635. https://onlinelibrary.wiley.com/doi/10.1002/asi.24638. Retrieved 2023-07-21.
176. ↑ "Ethics guidelines for trustworthy AI" (in en). European Commission. 2019-04-08. https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai.
177. ↑ "White Paper on Artificial Intelligence – a European approach to excellence and trust | Shaping Europe's digital future". 19 February 2020. https://ec.europa.eu/digital-single-market/en/news/white-paper-artificial-intelligence-european-approach-excellence-and-trust.
178. ↑ "Implementation Timeline | EU Artificial Intelligence Act" (in en-US). https://artificialintelligenceact.eu/implementation-timeline/.
179. ↑ "OECD AI Policy Observatory". https://www.oecd.ai/.
180. ↑ Recommendation on the Ethics of Artificial Intelligence. UNESCO. 2021. https://unesdoc.unesco.org/ark:/48223/pf0000381137.locale=en.
181. ↑ "UNESCO member states adopt first global agreement on AI ethics" (in en-gb). 2021-11-26. https://www.helsinkitimes.fi/themes/themes/science-and-technology/20454-unesco-member-states-adopt-first-global-agreement-on-ai-ethics.html.
182. ↑ "The Obama Administration's Roadmap for AI Policy". Harvard Business Review. 2016-12-21. ISSN 0017-8012. https://hbr.org/2016/12/the-obama-administrations-roadmap-for-ai-policy.
183. ↑ "Accelerating America's Leadership in Artificial Intelligence – The White House". https://trumpwhitehouse.archives.gov/articles/accelerating-americas-leadership-in-artificial-intelligence/.
184. ↑ "Request for Comments on a Draft Memorandum to the Heads of Executive Departments and Agencies, "Guidance for Regulation of Artificial Intelligence Applications"". 2020-01-13. https://www.federalregister.gov/documents/2020/01/13/2020-00261/request-for-comments-on-a-draft-memorandum-to-the-heads-of-executive-departments-and-agencies.
185. ↑ Sen. Thune, John [R-SD (2024-12-18). "Text - S.3312 - 118th Congress (2023-2024): Artificial Intelligence Research, Innovation, and Accountability Act of 2024". https://www.congress.gov/bill/118th-congress/senate-bill/3312/text.
186. ↑ "CCC Offers Draft 20-Year AI Roadmap; Seeks Comments". 2019-05-14. https://www.hpcwire.com/2019/05/14/ccc-offers-draft-20-year-ai-roadmap-seeks-comments/.
187. ↑ "Request Comments on Draft: A 20-Year Community Roadmap for AI Research in the US » CCC Blog". 13 May 2019. https://www.cccblog.org/2019/05/13/request-comments-on-draft-a-20-year-community-roadmap-for-ai-research-in-the-us/.
188. ↑ Script error: The function "in_lang" does not exist. Интеллектуальные правила — Kommersant, 25.11.2021
189. ↑ Grace K, Salvatier J, Dafoe A, Zhang B, Evans O (2018-05-03). "When Will AI Exceed Human Performance? Evidence from AI Experts". arXiv:1705.08807 [cs.AI].
190. ↑ "China wants to shape the global future of artificial intelligence" (in en). https://www.technologyreview.com/2018/03/16/144630/china-wants-to-shape-the-global-future-of-artificial-intelligence/.
191. ↑ Adam, David (2024-04-26). "Future of Humanity Institute shuts: what's next for 'deep future' research?" (in en). Nature 629 (8010): 16–17. doi:10.1038/d41586-024-01229-8. PMID 38671273. Bibcode: 2024Natur.629...16A. https://www.nature.com/articles/d41586-024-01229-8.
192. ↑ Floridi, Luciano; Cowls, Josh; Beltrametti, Monica; Chatila, Raja; Chazerand, Patrice; Dignum, Virginia; Luetge, Christoph; Madelin, Robert et al. (2018-12-01). "AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations" (in en). Minds and Machines 28 (4): 689–707. doi:10.1007/s11023-018-9482-5. ISSN 1572-8641. PMID 30930541.
193. ↑ "AI Governance" (in en). https://www.oxfordmartin.ox.ac.uk/ai-governance.
194. ↑ "Joanna J. Bryson". WIRED. https://www.wired.com/author/joanna-j-bryson/. Retrieved 13 January 2023.
195. ↑ "New Artificial Intelligence Research Institute Launches" (in en-US). 2017-11-20. https://engineering.nyu.edu/news/new-artificial-intelligence-research-institute-launches.
196. ↑ James J. Hughes, ed (15 March 2017). Surviving the machine age: intelligent technology and the transformation of human work. Cham, Switzerland: Palgrave Macmillan Cham. ISBN 978-3-319-51165-8. OCLC 976407024.
197. ↑ Danaher, John (2019). Automation and utopia: human flourishing in a world without work. Cambridge, Massachusetts: Harvard University Press. ISBN 978-0-674-24220-3. OCLC 1114334813.
198. ↑ "TUM Institute for Ethics in Artificial Intelligence officially opened" (in en). https://www.tum.de/nc/en/about-tum/news/press-releases/details/35727/.
199. ↑ Communications, Paul Karoff SEAS (2019-01-25). "Harvard works to embed ethics in computer science curriculum" (in en-US). https://news.harvard.edu/gazette/story/2019/01/harvard-works-to-embed-ethics-in-computer-science-curriculum/.
200. ↑ Lee, Jennifer (2020-02-08). "When Bias Is Coded Into Our Technology" (in en). NPR. https://www.npr.org/sections/codeswitch/2020/02/08/770174171/when-bias-is-coded-into-our-technology.
201. ↑ "How one conference embraced diversity" (in en). Nature 564 (7735): 161–162. 2018-12-12. doi:10.1038/d41586-018-07718-x. PMID 31123357.
202. ↑ Roose, Kevin (2020-12-30). "The 2020 Good Tech Awards" (in en-US). The New York Times. ISSN 0362-4331. https://www.nytimes.com/2020/12/30/technology/2020-good-tech-awards.html.
203. ↑ Lodge, Paul (2014). "Leibniz's Mill Argument Against Mechanical Materialism Revisited". Ergo: An Open Access Journal of Philosophy 1 (20201214). doi:10.3998/ergo.12405314.0001.003. ISSN 2330-4014.
204. ↑ Bringsjord, Selmer; Govindarajulu, Naveen Sundar (2020), Zalta, Edward N.; Nodelman, Uri, eds., Artificial Intelligence (Summer 2020 ed.), Metaphysics Research Lab, Stanford University, https://plato.stanford.edu/archives/sum2020/entries/artificial-intelligence/, retrieved 2023-12-08
205. ↑ Kulesz, O. (2018). "Culture, Platforms and Machines". UNESCO, Paris.
206. ↑ Jr, Henry C. Lucas (1999-04-29) (in en). Information Technology and the Productivity Paradox: Assessing the Value of Investing in IT. Oxford University Press. ISBN 978-0-19-802838-3. https://books.google.com/books?id=FzwnridL72IC&dq=digital+Much+of+his+work+was+then+spent+testing+the+boundaries+of+his+three+laws+to+see+where+they+would+break+down,+or+where+they+would+create+paradoxical+or+unanticipated+behavior.&pg=PP13. Retrieved 2024-02-21.
207. ↑ Asimov, Isaac (2008). I, Robot. New York: Bantam. ISBN 978-0-553-38256-3.
208. ↑ Bryson, Joanna; Diamantis, Mihailis; Grant, Thomas (September 2017). "Of, for, and by the people: the legal lacuna of synthetic persons". Artificial Intelligence and Law 25 (3): 273–291. doi:10.1007/s10506-017-9214-9.
209. ↑ "Principles of robotics". UK's EPSRC. September 2010. https://epsrc.ukri.org/research/ourportfolio/themes/engineering/activities/principlesofrobotics/.
210. ↑ Yudkowsky, Eliezer (July 2004). "Why We Need Friendly AI". http://www.asimovlaws.com/articles/archives/2004/07/why_we_need_fri_1.html.
211. ↑ Aleksander, Igor (March 2017). "Partners of Humans: A Realistic Assessment of the Role of Robots in the Foreseeable Future" (in en). Journal of Information Technology 32 (1): 1–9. doi:10.1057/s41265-016-0032-4. ISSN 0268-3962. http://journals.sagepub.com/doi/10.1057/s41265-016-0032-4. Retrieved 2024-02-21.
212. ↑ Evolving Robots Learn To Lie To Each Other , Popular Science, August 18, 2009
213. ↑ Bassett, Caroline; Steinmueller, Ed; Voss, Georgina. "Better Made Up: The Mutual Influence of Science Fiction and Innovation". Nesta. https://www.nesta.org.uk/report/better-made-up-the-mutual-influence-of-science-fiction-and-innovation/.
214. ↑ Velasco, Guille (2020-05-04). "Science-Fiction: A Mirror for the Future of Humankind" (in en-US). https://revistaidees.cat/en/science-fiction-favors-engaging-debate-on-artificial-intelligence-and-ethics/.
215. ↑ "Love, Death & Robots season 2, episode 1 recap - "Automated Customer Service"" (in en-GB). 2021-05-14. https://readysteadycut.com/2021/05/14/recap-love-death-and-robots-season-2-episode-1-automated-customer-service-netflix-series/.
216. ↑ Cave, Stephen, ed (14 February 2020). AI narratives: a history of imaginative thinking about intelligent machines (First ed.). Oxford: Oxford University Press. ISBN 978-0-19-258604-9. OCLC 1143647559.
217. ↑ Cerqui, Daniela; Warwick, Kevin (2008), "Re-Designing Humankind: The Rise of Cyborgs, a Desirable Goal?" (in en), Philosophy and Design (Dordrecht: Springer Netherlands): pp. 185–195, doi:10.1007/978-1-4020-6591-0_14, ISBN 978-1-4020-6590-3, http://link.springer.com/10.1007/978-1-4020-6591-0_14, retrieved 2020-11-11

## External links

* Ethics of Artificial Intelligence at the Internet Encyclopedia of Philosophy
* Ethics of Artificial Intelligence and Robotics at the Stanford Encyclopedia of Philosophy
* The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence
* Russell, S.; Hauert, S.; Altman, R.; Veloso, M. (May 2015). "Robotics: Ethics of artificial intelligence". Nature 521 (7553): 415–418. doi:10.1038/521415a. PMID 26017428. Bibcode: 2015Natur.521..415..
* BBC News: Games to take on a life of their own
* A short history of computer ethics
* AI Ethics Guidelines Global Inventory by Algorithmwatch
* Hagendorff, Thilo (March 2020). "The Ethics of AI Ethics: An Evaluation of Guidelines". Minds and Machines 30 (1): 99–120. doi:10.1007/s11023-020-09517-8.
* Sheludko, M. (December, 2023). Ethical Aspects of Artificial Intelligence: Challenges and Imperatives. Software Development Blog.
* Eisikovits, Nir. "AI Is an Existential Threat—Just Not the Way You Think" (in en). https://www.scientificamerican.com/article/ai-is-an-existential-threat-just-not-the-way-you-think/.
* Anwar U, Saparov A, Rando J, Paleka D, Turpin M, Hase P, Lubana ES, Jenner E, Casper S, Sourbut O, Edelman BL, Zhang Z, Günther M, Korinek A, Hernandez-Orallo J, Hammond L, Bigelow E, Pan A, Langosco L, Krueger D (2024). "Foundational Challenges in Assuring Alignment and Safety of Large Language Models". arXiv:2404.09932 [cs.LG].

* v
* t
* e

Ethics

Theories|

* Casuistry
* Consequentialism
* Deontology
* Kantian ethics
* Ethics of care
* Existentialist ethics
* Meta-ethics
* Particularism
* Pragmatic ethics
* Role ethics
* Virtue ethics

Concepts|

* Autonomy
* Axiology
* Belief
* Conscience
* Consent
* Equality
* Care
* Free will
* Good and evil
* Good
* Evil
* Happiness
* Ideal
* Immorality
* Justice
* Liberty
* Morality
* Norm
* Freedom
* Principles
* Suffering or Pain
* Stewardship
* Sympathy
* Trust
* Value
* Virtue
* World view
* Wrong
* full index...

Philosophers|

* Laozi
* Socrates
* Plato
* Aristotle
* Diogenes
* Valluvar
* Cicero
* Confucius
* Augustine of Hippo
* Mencius
* Mozi
* Xunzi
* Thomas Aquinas
* Baruch Spinoza
* David Hume
* Immanuel Kant
* Georg W. F. Hegel
* Arthur Schopenhauer
* Jeremy Bentham
* John Stuart Mill
* Søren Kierkegaard
* Henry Sidgwick
* Friedrich Nietzsche
* G. E. Moore
* Karl Barth
* Paul Tillich
* Dietrich Bonhoeffer
* Philippa Foot
* John Rawls
* John Dewey
* Bernard Williams
* J. L. Mackie
* G. E. M. Anscombe
* William Frankena
* Alasdair MacIntyre
* R. M. Hare
* Peter Singer
* Derek Parfit
* Thomas Nagel
* Robert Merrihew Adams
* Charles Taylor
* Joxe Azurmendi
* Christine Korsgaard
* Martha Nussbaum
* more...

Applied ethics|

* Bioethics
* Business ethics
* Discourse ethics
* Engineering ethics
* Environmental ethics
* Legal ethics
* Media ethics
* Medical ethics
* Nursing ethics
* Professional ethics
* Sexual ethics
* Ethics of eating meat
* Ethics of technology

Meta-ethics|

* Cognitivism
* Moral realism
* Ethical naturalism
* Ethical non-naturalism
* Ethical subjectivism
* Ideal observer theory
* Divine command theory
* Error theory
* Non-cognitivism
* Emotivism
* Quasi-realism
* Universal prescriptivism
* Moral universalism
* Value monism
* Value pluralism
* Moral relativism
* Moral nihilism
* Empiricism
* Moral rationalism
* Ethical intuitionism
* Moral skepticism

Related articles|

* Christian ethics
* Descriptive ethics
* Ethics in religion
* Evolutionary ethics
* Feminist ethics
* History of ethics
* Ideology
* Islamic ethics
* Jewish ethics
* Moral psychology
* Normative ethics
* Philosophy of law
* Political philosophy
* Population ethics
* Social philosophy

* Category

* v
* t
* e

Artificial intelligence (AI)

* History
* timeline
* Glossary
* Companies
* Projects

Concepts|

* Parameter
* Hyperparameter
* Loss functions
* Regression
* Bias–variance tradeoff
* Double descent
* Overfitting
* Clustering
* Gradient descent
* SGD
* Quasi-Newton method
* Conjugate gradient method
* Backpropagation
* Attention
* Convolution
* Normalization
* Batchnorm
* Activation
* Softmax
* Sigmoid
* Rectifier
* Gating
* Weight initialization
* Regularization
* Datasets
* Augmentation
* Prompt engineering
* Reinforcement learning
* Q-learning
* SARSA
* Imitation
* Policy gradient
* Diffusion
* Latent diffusion model
* Autoregression
* Adversary
* RAG
* Uncanny valley
* RLHF
* Self-supervised learning
* Reflection
* Recursive self-improvement
* Hallucination
* Word embedding
* Vibe coding

Applications|

* Machine learning
* In-context learning
* Artificial neural network
* Deep learning
* Language model
* Large
* NMT
* Reasoning
* Model Context Protocol
* Intelligent agent
* Artificial human companion
* Humanity's Last Exam
* Lethal autonomous weapons (LAWs)
* Generative artificial intelligence (GenAI)
* (Hypothetical: Artificial general intelligence (AGI))
* (Hypothetical: Artificial superintelligence (ASI))
* Agent2Agent protocol

Implementations|

| Audio–visual|

* AlexNet
* WaveNet
* Human image synthesis
* HWR
* OCR
* Computer vision
* Speech synthesis
* 15.ai
* ElevenLabs
* Speech recognition
* Whisper
* Facial recognition
* AlphaFold
* Text-to-image models
* Aurora
* DALL-E
* Firefly
* Flux
* GPT Image
* Ideogram
* Imagen
* Midjourney
* Recraft
* Stable Diffusion
* Text-to-video models
* Dream Machine
* Runway Gen
* Hailuo AI
* Kling
* Sora
* Seedance
* Veo
* Music generation
* Riffusion
* Suno AI
* Udio

|

Text|

* Word2vec
* Seq2seq
* GloVe
* BERT
* T5
* Llama
* Chinchilla AI
* PaLM
* GPT
* 1
* 2
* 3
* J
* ChatGPT
* 4
* 4o
* o1
* o3
* 4.5
* 4.1
* o4-mini
* 5
* 5.1
* 5.2
* Claude
* Gemini
* Gemini (language model)
* Gemma
* Grok
* LaMDA
* BLOOM
* DBRX
* Project Debater
* IBM Watson
* IBM Watsonx
* Granite
* PanGu-Σ
* DeepSeek
* Qwen

Decisional|

* AlphaGo
* AlphaZero
* OpenAI Five
* Self-driving car
* MuZero
* Action selection
* AutoGPT
* Robot control

People|

* Alan Turing
* Warren Sturgis McCulloch
* Walter Pitts
* John von Neumann
* Christopher D. Manning
* Claude Shannon
* Shun'ichi Amari
* Kunihiko Fukushima
* Takeo Kanade
* Marvin Minsky
* John McCarthy
* Nathaniel Rochester
* Allen Newell
* Cliff Shaw
* Herbert A. Simon
* Oliver Selfridge
* Frank Rosenblatt
* Bernard Widrow
* Joseph Weizenbaum
* Seymour Papert
* Seppo Linnainmaa
* Paul Werbos
* Geoffrey Hinton
* John Hopfield
* Jürgen Schmidhuber
* Yann LeCun
* Yoshua Bengio
* Lotfi A. Zadeh
* Stephen Grossberg
* Alex Graves
* James Goodnight
* Andrew Ng
* Fei-Fei Li
* Alex Krizhevsky
* Ilya Sutskever
* Oriol Vinyals
* Quoc V. Le
* Ian Goodfellow
* Demis Hassabis
* David Silver
* Andrej Karpathy
* Ashish Vaswani
* Noam Shazeer
* Aidan Gomez
* John Schulman
* Mustafa Suleyman
* Jan Leike
* Daniel Kokotajlo
* François Chollet

Architectures|

* Neural Turing machine
* Differentiable neural computer
* Transformer
* Vision transformer (ViT)
* Recurrent neural network (RNN)
* Long short-term memory (LSTM)
* Gated recurrent unit (GRU)
* Echo state network
* Multilayer perceptron (MLP)
* Convolutional neural network (CNN)
* Residual neural network (RNN)
* Highway network
* Mamba
* Autoencoder
* Variational autoencoder (VAE)
* Generative adversarial network (GAN)
* Graph neural network (GNN)

Political|

* AI safety (Alignment)
* Ethics of AI
* EU AI Act
* Precautionary principle
* Regulation of AI
* Virtual politician

Social and economic|

* AI boom
* AI bubble
* AI literacy
* AI slop
* AI veganism
* AI winter
* Anthropomorphism
* In architecture
* In education
* In healthcare
* Chatbot psychosis
* Mental health
* In visual art

* Category

* v
* t
* e

Existential risk from artificial intelligence

Concepts|

* AI box
* AI takeover
* Control problem
* Existential risk from artificial general intelligence
* Friendly artificial intelligence
* Instrumental convergence
* Intelligence explosion
* Machine ethics
* Superintelligence
* Technological singularity

Organizations|

* Allen Institute for Artificial Intelligence
* Center for Applied Rationality
* Center for Security and Emerging Technology
* Centre for the Study of Existential Risk
* DeepMind
* Foundational Questions Institute
* Future of Humanity Institute
* Future of Life Institute
* Humanity+
* Institute for Ethics and Emerging Technologies
* Leverhulme Centre for the Future of Intelligence
* Machine Intelligence Research Institute
* OpenAI

People|

* Nick Bostrom
* Sam Harris
* Stephen Hawking
* Bill Hibbard
* Bill Joy
* Elon Musk
* Steve Omohundro
* Huw Price
* Martin Rees
* Stuart J. Russell
* Jaan Tallinn
* Max Tegmark
* Frank Wilczek
* Roman Yampolskiy
* Andrew Yang
* Eliezer Yudkowsky

Other|

* Open Letter on Artificial Intelligence
* Ethics of artificial intelligence
* Controversies and dangers of artificial general intelligence
* Artificial intelligence as a global catastrophic risk
* Superintelligence
* Our Final Invention

Category

* v
* t
* e

Philosophy of science

| Concepts|

* Analysis
* Analytic–synthetic distinction
* A priori and a posteriori
* Causality
* Commensurability
* Consilience
* Construct
* Creative synthesis
* Demarcation problem
* Empirical evidence
* Explanatory power
* Fact
* Falsifiability
* Feminist method
* Functional contextualism

* Ignoramus et ignorabimus
* Inductive reasoning
* Intertheoretic reduction
* Inquiry
* Nature
* Objectivity
* Observation
* Paradigm
* Problem of induction
* Scientific law
* Scientific method
* Scientific revolution
* Scientific theory
* Testability
* Theory choice
* Theory-ladenness
* Underdetermination
* Unity of science

|

Metatheory
of science|

* Coherentism
* Confirmation holism
* Constructive empiricism
* Constructive realism
* Constructivist epistemology
* Contextualism
* Conventionalism
* Deductive-nomological model
* Hypothetico-deductive model
* Inductionism
* Epistemological anarchism
* Evolutionism
* Fallibilism
* Foundationalism
* Instrumentalism
* Pragmatism
* Model-dependent realism
* Naturalism
* Physicalism
* Positivism / Reductionism / Determinism
* Rationalism / Empiricism
* Received view / Semantic view of theories
* Scientific realism / Anti-realism
* Scientific essentialism
* Scientific formalism
* Scientific skepticism
* Scientism
* Structuralism
* Uniformitarianism
* Vitalism

Philosophy of|

* Physics
* thermal and statistical
* Motion
* Chemistry
* Biology
* Environment
* Geography
* Social science
* Technology
* Engineering
* Artificial intelligence
* Computer science
* Information
* Mind
* Psychiatry
* Psychology
* Perception
* Space and time

Related topics|

* Alchemy
* Criticism of science
* Epistemology
* Faith and rationality
* History and philosophy of science
* History of science
* History of evolutionary thought
* Logic
* Metaphysics
* Pseudoscience
* Relationship between religion and science
* Rhetoric of science
* Science studies
* Sociology of scientific knowledge
* Sociology of scientific ignorance

Philosophers of science by era

| Ancient|

* Plato
* Aristotle
* Stoicism
* Epicureans

|

Medieval|

* Averroes
* Avicenna
* Roger Bacon
* William of Ockham
* Hugh of Saint Victor
* Dominicus Gundissalinus
* Robert Kilwardby

Early modern|

* Francis Bacon
* Thomas Hobbes
* René Descartes
* Galileo Galilei
* Pierre Gassendi
* Isaac Newton
* David Hume

Late modern|

* Immanuel Kant
* Friedrich Schelling
* William Whewell
* Auguste Comte
* John Stuart Mill
* Herbert Spencer
* Wilhelm Wundt
* Charles Sanders Peirce
* Wilhelm Windelband
* Henri Poincaré
* Pierre Duhem
* Rudolf Steiner
* Karl Pearson

Contemporary|

* Alfred North Whitehead
* Bertrand Russell
* Albert Einstein
* Otto Neurath
* C. D. Broad
* Michael Polanyi
* Hans Reichenbach
* Rudolf Carnap
* Karl Popper
* Carl Gustav Hempel
* W. V. O. Quine
* Thomas Kuhn
* Imre Lakatos
* Paul Feyerabend
* Jürgen Habermas
* Ian Hacking
* Bas van Fraassen
* Larry Laudan
* Daniel Dennett

* Category
*   *

0.00

(0 votes)

|

Retrieved from "https://handwiki.org/wiki/index.php?title=Philosophy:Ethics_of_artificial_intelligence&oldid=4379710"
*[v]: View this template
*[t]: Discuss this template
*[e]: Edit this template