# Ethics of artificial intelligence

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Part of a series on Artificial intelligence (AI) Major goals Artificial general intelligence Intelligent agent Recursive self-improvement Planning Computer vision General game playing Knowledge representation Natural language processing Robotics AI safety Approaches Machine learning Symbolic Deep learning Bayesian networks Evolutionary algorithms Neuro-symbolic AI Systems integration Open-source AI data centers Applications Bioinformatics Deepfake Earth sciences Finance Generative AI Art Audio Music Government Healthcare Industry Software development Translation Military Physics Projects Philosophy AI alignment Artificial consciousness The bitter lesson Chinese room Friendly AI Ethics Existential risk Turing test Uncanny valley Human–AI interaction History Timeline Progress AI winter AI boom AI bubble Controversies Deepfake pornography Taylor Swift deepfake pornography controversy Grok sexual deepfake scandal Google Gemini image generation controversy It's the Most Terrible Time of the Year Pause Giant AI Experiments Removal of Sam Altman from OpenAI Statement on AI Risk Tay (chatbot) Théâtre D'opéra Spatial Voiceverse NFT plagiarism scandal Glossary Glossary v t e

The [ethics](/source/Ethics) of [artificial intelligence](/source/Artificial_intelligence) covers a broad range of topics within AI that are considered to have particular ethical stakes.[1] This includes [algorithmic biases](/source/Algorithmic_bias), [fairness](/source/Fairness_(machine_learning)), [accountability](/source/Accountability), transparency, privacy, and [regulation](/source/Regulation_of_artificial_intelligence), particularly where systems influence or automate human decision-making. It also covers various emerging or potential future challenges such as [machine ethics](/source/Machine_ethics) (how to make machines that behave ethically), [lethal autonomous weapon systems](/source/Lethal_autonomous_weapon), [arms race](/source/Artificial_intelligence_arms_race) dynamics, [AI safety](/source/AI_safety) and [alignment](/source/AI_alignment), [technological unemployment](/source/Technological_unemployment), AI-enabled [misinformation](/source/Misinformation),[2] how to treat certain AI systems if they have a [moral status](/source/Moral_status) (AI welfare and rights), [artificial superintelligence](/source/Artificial_superintelligence) and [existential risks](/source/Existential_risk_from_artificial_general_intelligence).[1]

Some application areas may also have particularly important ethical implications, like [healthcare](/source/Artificial_intelligence_in_healthcare), education, criminal justice, or the military.

## Machine ethics

Main articles: [Machine ethics](/source/Machine_ethics) and [AI alignment](/source/AI_alignment)

Machine ethics (or machine morality) is the field of research concerned with designing [Artificial Moral Agents](/source/Moral_agency#Artificial_Moral_Agents) (AMAs), robots or artificially intelligent computers that behave morally or as though moral.[3][4][5][6] To account for the nature of these agents, it has been suggested to consider certain philosophical ideas, like the standard characterizations of [agency](/source/Agency_(philosophy)), [rational agency](/source/Rational_agent), [moral agency](/source/Moral_agency), and artificial agency, which are related to the concept of AMAs.[7]

There are discussions on creating tests to see if an AI is capable of making [ethical decisions](/source/Ethical_decision). [Alan Winfield](/source/Alan_Winfield) concludes that the [Turing test](/source/Turing_test) is flawed and the requirement for an AI to pass the test is too low.[8] 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.[8] [Neuromorphic](/source/Neuromorphic_engineering) 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.[9] Similarly, [whole-brain emulation](/source/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.[10] And [large language models](/source/Large_language_model) are capable of approximating human moral judgments.[11] 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*,[12] [Wendell Wallach](/source/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](/source/Normative_ethics) 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](/source/List_of_machine_learning_algorithms) to use in machines. For simple decisions, [Nick Bostrom](/source/Nick_Bostrom) and [Eliezer Yudkowsky](/source/Eliezer_Yudkowsky) have argued that [decision trees](/source/Decision_tree) (such as [ID3](/source/ID3_algorithm)) are more transparent than [neural networks](/source/Artificial_neural_network) and [genetic algorithms](/source/Genetic_algorithm),[13] while Chris Santos-Lang argued in favor of [machine learning](/source/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](/source/Hacker_culture)".[14]

Some researchers frame machine ethics as part of the broader AI control or value alignment problem: the difficulty of ensuring that increasingly capable systems pursue objectives that remain compatible with human values and oversight. [Stuart Russell](/source/Stuart_Russell) has argued that beneficial systems should be designed to (1) aim at realizing human preferences, (2) remain uncertain about what those preferences are, and (3) learn about them from human behaviour and feedback, rather than optimizing a fixed, fully specified goal.[15] Some authors argue that apparent compliance with human values may reflect optimization for evaluation contexts rather than stable internal norms, complicating the assessment of alignment in advanced language models.[16]

## Challenges

### Algorithmic biases

Main article: [Algorithmic bias](/source/Algorithmic_bias)

[Kamala Harris](/source/Kamala_Harris) speaking about racial bias in AI in 2020

AI has become increasingly inherent in facial and [voice recognition](/source/Speech_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.[17][18][19][20]

According to Allison Powell, associate professor at [LSE](/source/London_School_of_Economics) 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.[21] Friedman and Nissenbaum identify three categories of bias in computer systems: existing bias, technical bias, and emergent bias.[22] In [natural language processing](/source/Natural_language_processing), problems can arise from the [text corpus](/source/Text_corpus)—the source material the algorithm uses to learn about the relationships between different words.[23]

Large companies such as IBM, Google, etc. that provide significant funding for research and development[24] have made efforts to research and address these biases.[25][26][27] One potential solution is to create documentation for the data used to train AI systems.[28][29] [Process mining](/source/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.[30] However, there are also limitations to the current landscape of [fairness in AI](/source/Fairness_(machine_learning)#Limitations), due to the intrinsic ambiguities in the concept of [discrimination](/source/Discrimination), both at the philosophical and legal level.[31][32]

#### Racial and gender biases

Bias can be introduced through historical data used to train AI systems.[33][34] For instance, [Amazon](/source/Amazon_(company)) terminated their use of [AI hiring and recruitment](/source/Artificial_intelligence_in_hiring) because the algorithm favored male candidates over female ones.[35] 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.[36]

The performance of [facial recognition](/source/Facial_recognition_system) and computer vision models may vary based on race and gender. Facial recognition algorithms made by Microsoft, IBM and Face++ all performed significantly worse on darker-skinned women.[37][38] 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](/source/Pulse_oximetry) that overestimated blood oxygen levels in patients with darker skin, causing issues with their [hypoxia](/source/Hypoxia_(medicine)) treatment.[39] In 2015, controversy erupted after a Black couple were labeled "Gorillas" by Google Photos.[40][41] 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](/source/U.S._states). 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](/source/Ethnicity). Biases often stem from the training data rather than the [algorithm](/source/Algorithm) itself, notably when the data represents past human decisions.[42]

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.[43]

[Injustice](/source/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.[44] 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](/source/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.[45]

In the justice system, AI can 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.[46] The [COMPAS](/source/COMPAS_(software)) 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".[47] 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.[48]

Large language models often reinforce [gender stereotypes](/source/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.[49][50][39] Additionally, [facial recognition](/source/Facial_recognition_system), [computer vision](/source/Computer_vision), or automatic gender recognition models can reinforce bias against both [cisgender](/source/Cisgender)[51][52] and [transgender](/source/Transgender)[53][54] people through misclassification of gender that is misaligned with the person's identity.

#### 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.[55][33] For instance, scholars highlighted how AI systems can reproduce and amplify global inequalities, particularly when data and model development are concentrated in Western countries, raising concerns about fairness and representation in AI systems.[56]

Such stereotypes stem directly from the design of AI systems and programmatic models from which they are trained. Stereotypes that target specific demographics originate from societal biases embedded during the programming process, outdated datasets, and algorithmic architectures that prioritize high-ranking and majority groups rather than underrepresented ones.[57] Research also amplifies user feedback as a primary contributor to stereotypes within AI, as human interactions introduce bias. Additionally, the AI industry is a male-dominant field, primarily young adult males, creating a lack of diversity that cultivates inequalities in AI databases. Word embeddings reveal that the use of "person/people" within AI algorithms displays gender inequality, as it prioritizes men over women rather than neutrality.[57]

#### Language bias

AI is primarily trained on English.[58] Celeste Rodriguez Louro has argued that mainstream American English is the primary variety of English used to train [generative AI](/source/Generative_AI) systems, resulting in a linguistic bias toward homogeneity and the exclusion of other varieties of English.[58] Since current large language models are predominantly trained on English-language data, they often present Western views as truth, while systematically downplaying non-English perspectives.[59] As of 2024, most AI systems are trained on only 100 of the 7,000 world languages.[60]

#### 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.[61][62][63] This skewing of the data is known as algorithmic bias, or when an AI has a predisposition to certain answers based on the data that the AI was trained on.[64] This can create an AI system that is not giving objective answers, but rather skewed answers that lean towards differing ends of the political spectrum. It has been found that users are more likely to agree with answers that coincide with their existing political beliefs. Some AI systems try to gauge the political affiliation of the user so that the generated answers can be politically skewed to align with the user,[65] leading to a never-ending confirmation bias loop. It is more difficult for users to perceive a political bias if they already align with the answer,[66] allowing these AI companies and programmers to ultimately get away with their politically biased AI models.

### Dominance by tech giants

The commercial AI scene is dominated by [Big Tech](/source/Big_Tech) companies, including [Alphabet Inc.](/source/Alphabet_Inc.), [Amazon](/source/Amazon_(company)), [Apple Inc.](/source/Apple_Inc.), [Meta Platforms](/source/Meta_Platforms), [Microsoft](/source/Microsoft), and SpaceX.[67][68][69] Some of these players already own the vast majority of existing [cloud infrastructure](/source/Cloud_computing) and [computing](/source/Computing) power from [data centers](/source/Data_center), allowing them to entrench further in the marketplace.[70][71] Their current dominance within the market of technology makes it very hard for newer companies to compete and be successful in the long-run within the industry.[72] It has been suggested by competition law scholars that the tech giants of the world may be using their power within the market to foreclose the market from potential competitors and, in turn, charge higher prices to consumers.[73] In light of some of these concerns, governments around the world have been considering and implementing laws that would prevent large companies from continuing or executing these practices.[73] These tech giants have the money that it takes to build the infrastructure needed nowadays. The five biggest are projected to spend $602 billion in 2026 on capital expenditures alone, which would be a 32% increase from the year prior.[72] In this spending, it is estimated that 75% will go towards AI-specific infrastructure.[72] With the significant growth that has been seen in the tech industry with AI, it is important to keep the industry competitive and fair.

### Climate impacts

Main article: [Environmental impact of artificial intelligence](/source/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](/source/Electronic_waste).[74] Despite improved energy efficiency, the energy needs are expected to increase, as AI gets more broadly used.[75]

#### 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.[74] 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.[76]

#### Water consumption

In addition to carbon emissions, these data centers also need water for cooling AI chips. Locally, this can lead to [water scarcity](/source/Water_scarcity) and the disruption of ecosystems. Around two liters of water are needed per kilowatt hour of energy used in a data center.[76] Over 80% of total water consumption comes from electricity generation that is used to fuel data centers. In addition to this, around 2/3 of data centers built are placed in water-scarce regions.[77] Because of this, AI development can compete with local communities and agriculture for water usage. Many companies do not fully disclose the severity of their impact on water consumption. Zero-water air-cooling systems exist but result in increased electricity usage and higher carbon emissions. Companies have to decide to prioritize the local concern of water usage or the global concern of carbon emissions. For an AI query, 16.9 mL of water is used, but only 2.2mL goes towards the cooling of the systems.[77] This is less than 15% of the total water used in the interaction.

#### Electronic waste

Another problem is the resulting electronic waste (or e-waste). This can include hazardous materials and chemicals, such as [lead](/source/Lead) and [mercury](/source/Mercury_(element)), 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.[76]

#### Prospective

The rising popularity of AI increases the need for data centers and intensifies these problems.[75] 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](/source/Fast_fashion), an industry that already produces significant emissions.[78]

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.[78]

### Open source

[Bill Hibbard](/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.[79] Organizations like [Hugging Face](/source/Hugging_Face)[80] and [EleutherAI](/source/EleutherAI)[81] have been actively open-sourcing AI software. Various open-weight large language models have also been released, such as [Gemma](/source/Gemma_(language_model)), [Llama2](/source/LLaMA) and [Mistral](/source/Mistral_AI).[82]

However, making code [open source](/source/Open_source) does not make it comprehensible, which by many definitions means that the AI code is not transparent. The [IEEE Standards Association](/source/IEEE_Standards_Association) has published a [technical standard](/source/Technical_standards) on Transparency of Autonomous Systems: IEEE 7001-2021.[83] The IEEE effort identifies multiple scales of transparency for different stakeholders.

There are also concerns that releasing AI models may lead to misuse.[84] 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.[85] Furthermore, open-weight AI models can be [fine-tuned](/source/Fine-tuning_(deep_learning)) 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](/source/Bioweapons) or to automate [cyberattacks](/source/Cyberattack).[86] [OpenAI](/source/OpenAI), initially committed to an open-source approach to the development of [artificial general intelligence](/source/Artificial_general_intelligence) (AGI), eventually switched to a closed-source approach, citing competitiveness and safety reasons. [Ilya Sutskever](/source/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.[87]

### Strain on open knowledge platforms

In April 2023, *[Wired](/source/Wired_(magazine))* reported that [Stack Overflow](/source/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](/source/Open_knowledge). Stack Overflow said its data was being accessed through [scraping](/source/Data_scraping), APIs, and data dumps, often without proper attribution, in violation of its terms and the [Creative Commons license](/source/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".[88]

Aggressive AI crawlers have increasingly overloaded open-source infrastructure, "causing what amounts to persistent [distributed denial-of-service](/source/Distributed_denial-of-service) (DDoS) attacks on vital public resources", according to a March 2025 *[Ars Technica](/source/Ars_Technica)* article. Projects like [GNOME](/source/GNOME), [KDE](/source/KDE), and [Read the Docs](/source/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](/source/Proof_of-work_system) and country blocks. According to the article, such unchecked scraping "risks severely damaging the very [digital ecosystem](/source/Digital_ecosystem) on which these AI models depend".[89]

In April 2025, the [Wikimedia Foundation](/source/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".[90]

### Transparency

Approaches like machine learning with [neural networks](/source/Neural_network) 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.[91] Consequently, many standards and policies have been proposed to compel developers of AI systems to incorporate transparency into their systems.[92] This push for transparency has led to advocacy and in some jurisdictions legal requirements for [explainable artificial intelligence](/source/Explainable_artificial_intelligence).[93] 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.[94]

In healthcare, the use of complex AI methods or techniques often results in models described as "[black-boxes](/source/Black_box)" 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.[95] Trust in healthcare AI has been shown to vary depending on the level of transparency provided.[96] Moreover, unexplainable outputs of AI systems make it much more difficult to identify and detect medical error.[97]

### Accountability

A special case of the opaqueness of AI is that caused by it being [anthropomorphised](/source/Anthropomorphised), that is, assumed to have human-like characteristics, resulting in misplaced conceptions of its [moral agency](/source/Moral_agency).[*[dubious](https://en.wikipedia.org/wiki/Wikipedia:Accuracy_dispute#Disputed_statement) – [discuss](https://en.wikipedia.org/wiki/Talk:Ethics_of_artificial_intelligence#Dubious)*] This can cause people to overlook whether either human [negligence](/source/Negligence) or deliberate criminal action has led to unethical outcomes produced through an AI system. Some recent [digital governance](/source/Digital_governance) regulations, such as [EU](/source/EU)'s [AI Act](/source/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](/source/Product_liability). This includes potentially [AI audits](/source/Information_technology_audit).

### Regulation

Main article: [Regulation of artificial intelligence](/source/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.[98] Similarly, according to a five-country study by KPMG and the [University of Queensland](/source/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.[99]

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.[100] The [OECD](/source/OECD), [UN](/source/UN), [EU](/source/EU), and many countries are presently working on strategies for regulating AI, and finding appropriate legal frameworks.[101][102][103][2]

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".[104] 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.[105] 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.[106] 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.[107]

In June 2024, the EU adopted the [Artificial Intelligence Act](/source/Artificial_Intelligence_Act) (AI Act).[108] On August 1st 2024, The AI Act [entered into force](/source/Entry_into_force).[109] The rules gradually apply, with the act becoming fully applicable 24 months after entry into force.[108] The AI Act sets rules on providers and users of AI systems.[108] 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.[109]

### Deepfakes

See also: [Deepfake pornography](/source/Deepfake_pornography) and [Grok sexual deepfake scandal](/source/Grok_sexual_deepfake_scandal)

[Deepfakes](/source/Deepfake) are digital media generated or altered with AI to impersonate someone. The term "deepfake" is a [portmanteau](/source/Portmanteau) of "[deep learning](/source/Deep_learning)" and "fake". It emerged on [Reddit](/source/Reddit) in 2017, after users began sharing non-consensually-generated pornographic deepfake videos. By the early 2020s, as generative AI became widely available, deepfake became a household term. Deepfake victims are frequently public figures engaging in controversial actions.[110] For example, in 2024, [pornographic deepfakes of Taylor Swift](/source/Taylor_Swift_deepfake_pornography_controversy) went viral on the social network [X](/source/X_(social_network)).[111] Deepfake audio and video have been used for scams, notably by impersonating company executives or close relatives and asking for bank transfers.[112][113]

### 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.[33] 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](/source/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.[46] As [Tensor Processing Units](/source/Tensor_Processing_Unit) (TPUs) and [graphics processing units](/source/Graphics_processing_unit) (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](/source/Clinical_decision_support_system) (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.[114]

### AI welfare

See also: [Artificial intelligence § AI welfare and rights](/source/Artificial_intelligence#AI_welfare_and_rights), and [Sentientism](/source/Sentientism)

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](/source/Global_workspace_theory) or the [integrated information theory](/source/Integrated_information_theory). Edelman notes one exception had been [Thomas Metzinger](/source/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](/source/Suffering_risks)", 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.[115][116] Podcast host Dwarkesh Patel said he cared about making sure no "digital equivalent of [factory farming](/source/Factory_farming)" happens.[117] In the [ethics of uncertain sentience](/source/Ethics_of_uncertain_sentience), the [precautionary principle](/source/Precautionary_principle) is often invoked.[118]

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.[119] These include [OpenAI](/source/OpenAI) founder [Ilya Sutskever](/source/Ilya_Sutskever) in February 2022, when he wrote that today's large neural nets may be "slightly conscious". In November 2022, [David Chalmers](/source/David_Chalmers) argued that it was unlikely current large language models like [GPT-3](/source/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.[116][115][120] [Anthropic](/source/Anthropic) hired its first AI welfare researcher in 2024,[121] 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.[122]

According to Carl Shulman and [Nick Bostrom](/source/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](/source/Subjective_experience). These machines could also be engineered to feel intense and positive subjective experience, unaffected by the [hedonic treadmill](/source/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.[123][124]

### Threat to human dignity

Main article: [Computer Power and Human Reason](/source/Computer_Power_and_Human_Reason)

[Joseph Weizenbaum](/source/Joseph_Weizenbaum)[125] 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](/source/Interactive_voice_response) systems)

- A nursemaid for the elderly (as was reported by [Pamela McCorduck](/source/Pamela_McCorduck) in her book *The Fifth Generation*)

- A soldier

- A judge

- A police officer

- A therapist (as was proposed by [Kenneth Colby](/source/Kenneth_Colby) in the 1970s)

Weizenbaum says that humans require authentic feelings of [empathy](/source/Empathy) from people in these positions. If machines replace humans, we will find ourselves alienated, devalued and frustrated, for the AI 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."[126][*[failed verification](https://en.wikipedia.org/wiki/Wikipedia:Verifiability)*]

[Pamela McCorduck](/source/Pamela_McCorduck) counters that, speaking for women and minorities "I'd rather take my chances with an impartial computer", arguing that there are conditions where it would preferable to have automated judges and police that have no personal agenda at all.[126] However, [Kaplan](/source/Andreas_Kaplan) and Haenlein stressed in 2019 that such 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.[127]

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](/source/Computationalism)). To Weizenbaum, these points suggest that AI research devalues human life.[125]

AI founder [John McCarthy](/source/John_McCarthy_(computer_scientist)) objects to the moralizing tone of Weizenbaum's critique. "When moralizing is both vehement and vague, it invites authoritarian abuse", he writes. [Bill Hibbard](/source/Bill_Hibbard)[128] 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: [Self-driving car liability](/source/Self-driving_car_liability)

As the widespread use of [autonomous cars](/source/Self-driving_car) becomes increasingly imminent, new challenges raised by fully autonomous vehicles must be addressed.[129][130] There have been debates about the legal liability of the responsible party if these cars get into accidents.[131][132] 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.[133]

In another incident on March 18, 2018, [Elaine Herzberg](/source/Elaine_Herzberg) was struck and killed by a self-driving [Uber](/source/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.[134]

Currently, self-driving cars are considered semi-autonomous, requiring the driver to pay attention and be prepared to take control if necessary.[135][*[failed verification](https://en.wikipedia.org/wiki/Wikipedia:Verifiability)*] 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.[136][137][138]

Experts contend that autonomous vehicles ought to be able to distinguish between rightful and harmful decisions since they have the potential of inflicting harm.[139] 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](/source/Military_applications_of_artificial_intelligence) and [Lethal autonomous weapon](/source/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.[140] 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.[141][142] The President of the [Association for the Advancement of Artificial Intelligence](/source/Association_for_the_Advancement_of_Artificial_Intelligence) has commissioned a study to look at this issue.[143] They point to programs like the Language Acquisition Device which can emulate human interaction.

On October 31, 2019, the [United States Department of Defense](/source/United_States_Department_of_Defense)'s Defense Innovation Board published the draft of a report recommending principles for the ethical use of AI by the Department of Defense that would ensure a human operator would always be able to look into the '[black box](/source/Black_box)' and understand the kill-chain process. However, a major concern is how the report will be implemented.[144] The US Navy has funded a report which indicates that as [military robots](/source/Military_robots) become more complex, there should be greater attention to implications of their ability to make autonomous decisions.[141][142] Some researchers state that [autonomous robots](/source/Autonomous_robot) might be more humane, as they could make decisions more effectively.[145] In 2024, the [Defense Advanced Research Projects Agency](/source/DARPA) 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.[146][147]

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."[148] From a [consequentialist](/source/Consequentialism) 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](/source/Morality) framework that the AI cannot override.[149]

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](/source/AI_takeover). 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](/source/Unmanned_combat_aerial_vehicle), paralleling similar announcements by Russia and South Korea[150] respectively. Due to the potential of AI weapons becoming more dangerous than human-operated weapons, [Stephen Hawking](/source/Stephen_Hawking) and [Max Tegmark](/source/Max_Tegmark) signed a "Future of Life" petition[151] 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.[152]

"If any major military power pushes ahead with the AI weapon development, a global [arms race](/source/Arms_race) is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the [Kalashnikovs](/source/AK-47) of tomorrow", says the petition, which includes [Skype](/source/Skype) co-founder [Jaan Tallinn](/source/Jaan_Tallinn) and MIT professor of linguistics [Noam Chomsky](/source/Noam_Chomsky) as additional supporters against AI weaponry.[153]

Physicist and Astronomer Royal [Sir Martin Rees](/source/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](/source/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](/source/Centre_for_the_Study_of_Existential_Risk) at Cambridge University in the hope of avoiding this threat to human existence.[152]

Regarding the potential for smarter-than-human systems to be employed militarily, the [Open Philanthropy Project](/source/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](/source/Machine_Intelligence_Research_Institute) (MIRI) and the [Future of Humanity Institute](/source/Future_of_Humanity_Institute) (FHI), and there seems to have been less analysis and debate regarding them".[154]

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.[155]: 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.[155]: 91

Under the framework of the [Convention on Certain Conventional Weapons](/source/Convention_on_Certain_Conventional_Weapons), states have discussed lethal autonomous weapon systems since 2014. In 2016, the treaty's states parties established an open-ended [Group of Governmental Experts on Lethal Autonomous Weapons Systems](/source/Group_of_Governmental_Experts_on_Lethal_Autonomous_Weapons_Systems) to continue those discussions.[156] The discussions have addressed international humanitarian law, accountability, possible prohibitions and regulations, and the extent of human control required over AI-enabled weapons.[157]

A [summit](/source/Summit_on_Responsible_Artificial_Intelligence_in_the_Military_Domain) was held in 2023 in the Hague on the issue of using AI responsibly in the military domain.[158]

### Singularity

Further information: [Existential risk from artificial general intelligence](/source/Existential_risk_from_artificial_general_intelligence), [Superintelligence](/source/Superintelligence), and [Technological singularity](/source/Technological_singularity)

[Vernor Vinge](/source/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](/source/Technological_singularity)"[159] and is the central point of discussion in the philosophy of [Singularitarianism](/source/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 AI 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](/source/Intelligence_explosion), a self-improving AI could become so powerful that humans would not be able to stop it from achieving its goals.[160] In his paper "Ethical Issues in Advanced Artificial Intelligence" and subsequent book *[Superintelligence: Paths, Dangers, Strategies](/source/Superintelligence%3A_Paths%2C_Dangers%2C_Strategies)*, philosopher [Nick Bostrom](/source/Nick_Bostrom) argues that AI has the capability to bring about human extinction. He claims that an [artificial superintelligence](/source/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](/source/Unintended_consequences) could arise. It could kill off all other agents, persuade them to change their behavior, or block their attempts at interference.[161][162]

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](/source/Human_enhancement).[163]

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](/source/Eliezer_Yudkowsky), there is little reason to suppose that an artificially designed mind would have such an adaptation.[164] AI researchers such as [Stuart J. Russell](/source/Stuart_J._Russell),[165] [Bill Hibbard](/source/Bill_Hibbard),[128] [Roman Yampolskiy](/source/Roman_Yampolskiy),[166] [Shannon Vallor](/source/Shannon_Vallor),[167] [Steven Umbrello](https://en.wikipedia.org/w/index.php?title=Steven_Umbrello&action=edit&redlink=1)[168] and [Luciano Floridi](/source/Luciano_Floridi)[169] 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](/source/Nvidia)'s [Llama](/source/Llama_(language_model)) Guard, which focuses on improving the [safety](/source/AI_safety) and [alignment](/source/AI_alignment) of large AI models,[170] and [Preamble](/source/Preamble_(company))'s customizable guardrail platform.[171] These systems aim to address issues such as algorithmic bias, misuse, and vulnerabilities, including [prompt injection](/source/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.[171] Other tools focus on applying structured constraints to inputs, restricting outputs to predefined parameters,[172] or leveraging real-time monitoring mechanisms to identify and address vulnerabilities.[173] 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.[174][175]

## Institutions in AI policy and ethics

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

[Amazon](/source/Amazon.com%2C_Inc.), [Google](/source/Google), [Facebook](/source/Facebook), [IBM](/source/IBM), and [Microsoft](/source/Microsoft) have established a [non-profit](/source/Nonprofit_organization), 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.[176]

The [IEEE](/source/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](https://standards.ieee.org/industry-connections/activities/ieee-global-initiative/) 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](/source/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](/source/NGO) 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.[177]

### Intergovernmental initiatives

- The [European Commission](/source/European_Commission) has a High-Level Expert Group on Artificial Intelligence. On 8 April 2019, this published its "Ethics Guidelines for [Trustworthy Artificial Intelligence](/source/Trustworthy_AI)".[178] 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.[179] The European Commission also proposed the [Artificial Intelligence Act](/source/Artificial_Intelligence_Act), which came [into force](/source/Entry_into_force) on 1 August 2024, with provisions that shall come into operation gradually over time.[180]

- The [OECD](/source/OECD) established an OECD AI Policy Observatory.[181]

- In 2021, [UNESCO](/source/UNESCO) adopted the Recommendation on the Ethics of Artificial Intelligence,[182] the first global standard on the ethics of AI.[183]

### Governmental initiatives

- In the [United States](/source/United_States) the [Obama](/source/Obama) administration put together a Roadmap for AI Policy.[184] The Obama Administration released two prominent [white papers](/source/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).[185]

- In January 2020, in the United States, the [Trump Administration](/source/First_presidency_of_Donald_Trump) 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.[186]

- The Artificial Intelligence Research, Innovation, and Accountability Act of 2024 was a proposed bipartisan bill introduced by U.S. Senator [John Thune](/source/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](/source/National_Institute_of_Standards_and_Technology) for oversight based on pre-defined assessment criteria.[187]

- The [Computing Community Consortium (CCC)](/source/Computing_Community_Consortium) weighed in with a 100-plus page draft report[188] – *A 20-Year Community Roadmap for Artificial Intelligence Research in the US*[189]

- The [Center for Security and Emerging Technology](/source/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](https://en.wikipedia.org/w/index.php?title=Analytical_Center_for_the_Government_of_the_Russian_Federation&action=edit&redlink=1) together with major commercial and academic institutions such as [Sberbank](/source/Sberbank), [Yandex](/source/Yandex), [Rosatom](/source/Rosatom), [Higher School of Economics](/source/Higher_School_of_Economics), [Moscow Institute of Physics and Technology](/source/Moscow_Institute_of_Physics_and_Technology), [ITMO University](/source/ITMO_University), [Nanosemantics](/source/Nanosemantics), [Rostelecom](/source/Rostelecom), [CIAN](/source/CIAN) and others.[190]

- In [China](/source/China), the National Professional Committee on Next-Generation AI Governance issued the "Ethical Norms for the Next-Generation Artificial Intelligence" on September 25, 2021. The document outlines six basic requirements: enhancing human well-being, promoting fairness and justice, protecting privacy and safety, ensuring controllability and trustworthiness, strengthening responsibility, and improving ethical literacy. It also provides 18 specific norms for management, research and development, supply, and utilization activities.[191] In November 2022, China submitted a "Position Paper on Strengthening the Ethical Governance of Artificial Intelligence" to the [United Nations](/source/United_Nations) Convention on Certain Conventional Weapons (CCW) meeting. The paper advocates for the principle of "ethics first," the establishment and improvement of AI ethical rules, norms, and accountability mechanisms, and calls for the international community to reach international agreements based on broad participation.[192]

### Academic initiatives

- Multiple research institutes at the [University of Oxford](/source/University_of_Oxford) have centrally focused on AI ethics. The [Future of Humanity Institute](/source/Future_of_Humanity_Institute) focused on AI safety[193] and the governance of AI[194] before shuttering in 2024.[195] The Institute for Ethics in AI, directed by [John Tasioulas](/source/John_Tasioulas), whose primary goal, among others, is to promote AI ethics as a field proper in comparison to related [applied ethics](/source/Applied_ethics) fields. The [Oxford Internet Institute](/source/Oxford_Internet_Institute), directed by [Luciano Floridi](/source/Luciano_Floridi), focuses on the ethics of near-term AI technologies and ICTs.[196] The AI Governance Initiative at the Oxford Martin School focuses on understanding risks from AI from technical and policy perspectives.[197]

- The Centre for Digital Governance at the [Hertie School](/source/Hertie_School) in Berlin was co-founded by [Joanna Bryson](/source/Joanna_Bryson) to research questions of ethics and technology.[198]

- The [AI Now Institute](/source/AI_Now_Institute) at [NYU](/source/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.[199]

- The [Institute for Ethics and Emerging Technologies](/source/Institute_for_Ethics_and_Emerging_Technologies) (IEET) researches the effects of AI on unemployment,[200][201] and policy.

- The [Institute for Ethics in Artificial Intelligence](/source/Institute_for_Ethics_in_Artificial_Intelligence) (IEAI) at the [Technical University of Munich](/source/Technical_University_of_Munich) directed by [Christoph Lütge](/source/Christoph_L%C3%BCtge) conducts research across various domains such as mobility, employment, healthcare and sustainability.[202]

- [Barbara J. Grosz](/source/Barbara_J._Grosz), the Higgins Professor of Natural Sciences at the [Harvard John A. Paulson School of Engineering and Applied Sciences](/source/Harvard_John_A._Paulson_School_of_Engineering_and_Applied_Sciences) has initiated the Embedded EthiCS into [Harvard](/source/Harvard_University)'s computer science curriculum to develop a future generation of computer scientists with worldview that takes into account the social impact of their work.[203]

### Private organizations

- [Algorithmic Justice League](/source/Algorithmic_Justice_League)[204]

- [Black in AI](/source/Black_in_AI)[205]

- [Data for Black Lives](/source/Data_for_Black_Lives)[206]

## History

Historically speaking, the investigation of moral and ethical implications of "thinking machines" goes back at least to the [Enlightenment](/source/Age_of_Enlightenment): [Leibniz](/source/Gottfried_Wilhelm_Leibniz) already posed the question of whether we should attribute intelligence to a mechanism that behaves as if it were a sentient being,[207] and so does [Descartes](/source/Ren%C3%A9_Descartes), who describes what could be considered an early version of the [Turing test](/source/Turing_test).[208]

The [romantic](/source/Romanticism) period has several times envisioned artificial creatures that escape the control of their creator with dire consequences, most famously in [Mary Shelley](/source/Mary_Shelley)'s *[Frankenstein](/source/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*](/source/R.U.R.), [Karel Čapek](/source/Karel_%C4%8Capek)'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*)[209] but was also an international success after it premiered in 1921. [George Bernard Shaw](/source/George_Bernard_Shaw)'s play *[Back to Methuselah](/source/Back_to_Methuselah)*, published in 1921, questions at one point the validity of thinking machines that act like humans; [Fritz Lang](/source/Fritz_Lang)'s 1927 film *[Metropolis](/source/Metropolis_(1927_film))* shows an [android](/source/Android_(robot)) leading the uprising of the exploited masses against the oppressive regime of a [technocratic](/source/Technocracy) society.

In the 1950s, [Norbert Wiener](/source/Norbert_Wiener) wrote [The Human Use of Human Beings](/source/The_Human_Use_of_Human_Beings), discussing how humans and machines could cooperate. He explored the risk that such changes might harm society through dehumanization or subordination of humanity. Wiener offered suggestions on how to avoid such risk. [Isaac Asimov](/source/Isaac_Asimov) considered the issue of how to control machines in *[I, Robot](/source/I%2C_Robot)*. At the insistence of his editor [John W. Campbell Jr.](/source/John_W._Campbell_Jr.), he proposed the [Three Laws of Robotics](/source/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.[210] His work suggests that no set of fixed laws can sufficiently anticipate all possible circumstances.[211]

More recently, academics and many governments have challenged the idea that AI can itself be held accountable.[212] A panel convened by the [United Kingdom](/source/United_Kingdom) in 2010 revised Asimov's laws to clarify that AI is the responsibility either of its manufacturers, or of its owner/operator.[213] [Eliezer Yudkowsky](/source/Eliezer_Yudkowsky), from the [Machine Intelligence Research Institute](/source/Machine_Intelligence_Research_Institute), suggested in 2004 a need to study how to build a "[Friendly AI](/source/Friendly_AI)", meaning that there should also be efforts to make AI intrinsically friendly and humane.[214]

In 2009, academics and technical experts attended a conference organized by the [Association for the Advancement of Artificial Intelligence](/source/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.[215] 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.[159]

Also in 2009, during an experiment at the Laboratory of Intelligent Systems in the Ecole Polytechnique Fédérale of [Lausanne](/source/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.[216]

## Role and impact of fiction

Main article: [Artificial intelligence in fiction](/source/Artificial_intelligence_in_fiction)

The role of fiction with regards to AI ethics has been a complex one.[217] 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,[218] 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](/source/List_of_artificial_intelligence_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](/source/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](/source/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*](/source/Osmosis_(TV_series)) (2020) and British series [*The One*](/source/The_One_(TV_series)) 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*](/source/Love%2C_Death_%26_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.[219]

### Future visions in fiction and games

The movie *[The Thirteenth Floor](/source/The_Thirteenth_Floor)* suggests a future where [simulated worlds](/source/Simulated_reality) with sentient inhabitants are created by computer [game consoles](/source/Game_console) for the purpose of entertainment. The movie *[The Matrix](/source/The_Matrix)* suggests a future where the dominant species on planet Earth are sentient machines and humanity is treated with utmost [speciesism](/source/Speciesism). The short story "[The Planck Dive](/source/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](/source/Emergency_Medical_Hologram) of *[Starship Voyager](/source/USS_Voyager_(NCC-74656))*, which is an apparently sentient copy of a reduced subset of the consciousness of its creator, [Dr. Zimmerman](/source/Lewis_Zimmerman), who, for the best motives, has created the system to give medical assistance in case of emergencies. The movies *[Bicentennial Man](/source/Bicentennial_Man_(film))* and *[A.I.](/source/A.I._Artificial_Intelligence)* deal with the possibility of sentient robots that could love. *[I, Robot](/source/I%2C_Robot_(film))* explored some aspects of Asimov's three laws. All these scenarios try to foresee possibly unethical consequences of the creation of sentient computers.[220]

Over time, debates have tended to focus less and less on *possibility* and more on *desirability*,[221] as emphasized in the ["Cosmist" and "Terran" debates](/source/Hugo_de_Garis#The_Artilect_War) initiated by [Hugo de Garis](/source/Hugo_de_Garis) and [Kevin Warwick](/source/Kevin_Warwick).

## See also

- [AI takeover](/source/AI_takeover)

- [AI washing](/source/AI_washing)

- [Artificial consciousness](/source/Artificial_consciousness)

- [Artificial intelligence and copyright](/source/Artificial_intelligence_and_copyright)

- [Artificial general intelligence](/source/Artificial_general_intelligence) (AGI)

- [Computer ethics](/source/Computer_ethics)

- [Dead internet theory](/source/Dead_internet_theory)

- [Data colonialism](/source/Data_colonialism)

- [Effective altruism, the long term future and global catastrophic risks](/source/Effective_altruism#Long-term_future_and_global_catastrophic_risks)

- [Artificial intelligence and elections](/source/Artificial_intelligence_and_elections) – Use of AI in elections and political campaigning.

- [Ethics of uncertain sentience](/source/Ethics_of_uncertain_sentience)

- [Existential risk from artificial general intelligence](/source/Existential_risk_from_artificial_general_intelligence)

- *[Human Compatible](/source/Human_Compatible)*

- [Metaverse law](/source/Metaverse_law)

- [Personhood](/source/Personhood)

- [Philosophy of artificial intelligence](/source/Philosophy_of_artificial_intelligence)

- [Regulation of artificial intelligence](/source/Regulation_of_artificial_intelligence)

- [Robotic Governance](/source/Robotic_governance)

- [Roko's basilisk](/source/Roko's_basilisk)

- *[Superintelligence: Paths, Dangers, Strategies](/source/Superintelligence%3A_Paths%2C_Dangers%2C_Strategies)*

- [Suffering risks](/source/Suffering_risks)

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## Further reading

- [Olivier Sibony](/source/Olivier_Sibony) and [Éric Hazan](/source/%C3%89ric_Hazan_(entrepreneur)): *Faut-il encore décider? La décision humaine à l'ère de l'intelligence artificielle*, Flammarion, 2026.

## External links

- [Ethics of Artificial Intelligence](https://iep.utm.edu/ethics-of-artificial-intelligence/) at the [Internet Encyclopedia of Philosophy](/source/Internet_Encyclopedia_of_Philosophy)

- [Ethics of Artificial Intelligence and Robotics](https://plato.stanford.edu/entries/ethics-ai/) at the [Stanford Encyclopedia of Philosophy](/source/Stanford_Encyclopedia_of_Philosophy)

- [The Cambridge Handbook of the Law, Ethics and Policy of Artificial Intelligence](https://www.cambridge.org/core/books/cambridge-handbook-of-the-law-ethics-and-policy-of-artificial-intelligence/0AD007641DE27F837A3A16DBC0888DD1)

- Russell S, Hauert S, Altman R, Veloso M (May 2015). ["Robotics: Ethics of artificial intelligence"](https://doi.org/10.1038%2F521415a). *Nature*. **521** (7553): 415–418. [Bibcode](/source/Bibcode_(identifier)):[2015Natur.521..415.](https://ui.adsabs.harvard.edu/abs/2015Natur.521..415.). [doi](/source/Doi_(identifier)):[10.1038/521415a](https://doi.org/10.1038%2F521415a). [PMID](/source/PMID_(identifier)) [26017428](https://pubmed.ncbi.nlm.nih.gov/26017428). [S2CID](/source/S2CID_(identifier)) [4452826](https://api.semanticscholar.org/CorpusID:4452826).

- [AI Ethics Guidelines Global Inventory](https://algorithmwatch.org/en/project/ai-ethics-guidelines-global-inventory/) by [Algorithmwatch](https://algorithmwatch.org)

- Hagendorff T (March 2020). ["The Ethics of AI Ethics: An Evaluation of Guidelines"](https://doi.org/10.1007%2Fs11023-020-09517-8). *Minds and Machines*. **30** (1): 99–120. [arXiv](/source/ArXiv_(identifier)):[1903.03425](https://arxiv.org/abs/1903.03425). [doi](/source/Doi_(identifier)):[10.1007/s11023-020-09517-8](https://doi.org/10.1007%2Fs11023-020-09517-8). [S2CID](/source/S2CID_(identifier)) [72940833](https://api.semanticscholar.org/CorpusID:72940833).

- Sheludko, M. (December, 2023). [Ethical Aspects of Artificial Intelligence: Challenges and Imperatives](https://lasoft.org/blog/ethical-aspects-of-artificial-intelligence-challenges-and-imperatives/). Software Development Blog.

- Eisikovits N. ["AI Is an Existential Threat—Just Not the Way You Think"](https://www.scientificamerican.com/article/ai-is-an-existential-threat-just-not-the-way-you-think/). *Scientific American*. Retrieved 2024-03-04.

- 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](/source/ArXiv_(identifier)):[2404.09932](https://arxiv.org/abs/2404.09932) [[cs.LG](https://arxiv.org/archive/cs.LG)].

v t e Ethics Normative Consequentialism Deontology Care Particularism Pragmatic Role Suffering-focused Utilitarianism Virtue Applied Animal Insects Artificial intelligence Bio Business Computer Discourse Economic Engineering Environmental Land Legal Machine Marketing Meat eating Media Medical Nursing Professional Programming Research Sexual Technology Terraforming Uncertain sentience Work Meta Absolutism Axiological Cognitivism Realism Naturalism Non-naturalism Subjectivism Ideal observer theory Divine command theory Constructivism Euthyphro dilemma Intuitionism Nihilism Non-cognitivism Emotivism Expressivism Quasi-realism Universal prescriptivism Rationalism Relativism Skepticism Universalism Value monism vs. value pluralism Schools Buddhist Christian Protestant Confucian Epicurean Existentialist Feminist Islamic Jewish Kantian Rousseauian Stoic Tao Concepts Accountability Authority Autonomy Blame Common sense Compassion Conscience Consent Culture of life Desert Dignity Double standard Duty Equality Etiquette Eudaimonia Family values Fidelity Free will Good and evil Good Evil Problem of evil Greed Happiness Honour Ideal Immorality Importance Justice Liberty Loyalty Moral agency Moral circle expansion Moral courage Moral hierarchy Moral imperative Morality Norm Pacifism Political freedom Precept Punishment Rights Self-discipline Suffering Stewardship Sympathy Theodicy Torture Trust Utility Value Instrumental Intrinsic Japanese Vice Virtue Vow Wrong Ethicists Confucius Mozi Socrates Plato Aristotle Diogenes Mencius Xunzi Cicero Augustine Valluvar Aquinas Spinoza Butler Hume Smith Kant Hegel Schopenhauer Bentham Mill Kierkegaard Sidgwick Nietzsche Moore Barth Tillich Bonhoeffer Foot Rawls Dewey Williams Mackie Anscombe Frankena MacIntyre Hare Singer Parfit Nagel Adams Taylor Azurmendi Korsgaard Nussbaum Works Nicomachean Ethics (c. 322 BC) Tirukkural (c. 450 CE) Ethics (1677) A Treatise of Human Nature (1740) The Theory of Moral Sentiments (1759) An Introduction to the Principles of Morals and Legislation (1780) Groundwork of the Metaphysics of Morals (1785) Elements of the Philosophy of Right (1820) Either/Or (1843) Utilitarianism (1861) The Methods of Ethics (1874) On the Genealogy of Morality (1887) Principia Ethica (1903) The Right and the Good (1930) A Theory of Justice (1971) Animal Liberation (1975) After Virtue (1981) Reasons and Persons (1984) Related Axiology Casuistry Descriptive ethics Ethics in religion Evolutionary ethics History of ethics Human rights Ideology Moral psychology Philosophy of law Political philosophy Population ethics Rehabilitation Secular ethics Social philosophy Category Outline Portal WikiProject

v t e Artificial intelligence (AI) History timeline Glossary Companies Projects List of open-source AI software Concepts Automated reasoning 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 Symbolic AI Neuro-symbolic AI Applications Automated theorem proving Machine learning In-context learning Artificial neural network Deep learning Language model Large NMT Reasoning Model Context Protocol Intelligent agent AI agent Artificial human companion Humanity's Last Exam Lethal autonomous weapons (LAWs) Generative AI Weak AI Hypothetical Artificial general intelligence (AGI) 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 Udio World models Genie Oasis Text List of large language models Project Debater IBM Watson IBM Watsonx Decisional AlphaGo AlphaZero OpenAI Five Self-driving car MuZero Action selection AutoGPT Robot control Reasoning systems Deductive classifiers Expert systems Inference engines Knowledge-based systems Logic programs Procedural reasoning systems Semantic reasoners Rule-based systems Cognitive architectures ACT-R Soar CLARION LIDA OpenCog Knowledge bases ConceptNet Wikidata DBpedia YAGO 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 Neural network 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 Cold War AI safety (Alignment) AI takeover Elections Ethics of AI EU AI Act Nationalism Precautionary principle Regulation of AI US Virtual politician Propaganda Social and economic AI boom AI bubble AI data center AI effect AI literacy AI slop AI winter Anthropomorphism Arms race Competition Environmental impact Explainable AI Generative engine optimization In architecture In education In fiction In healthcare Chatbot psychosis In marketing In video games In visual art Military applications AI warfare Workplace impact Category

v t e Existential risk from artificial intelligence Concepts AGI AI alignment AI boom AI capability control AI safety AI successionism AI takeover Deceptive alignment Effective accelerationism Ethics of artificial intelligence Existential risk from artificial intelligence Friendly artificial intelligence Instrumental convergence Intelligence explosion Longtermism Machine ethics Suffering risks Superintelligence Technological singularity Vulnerable world hypothesis Organizations AI Futures Project Alignment Research Center Center for AI Safety Center for Applied Rationality Center for Human-Compatible Artificial Intelligence Centre for the Study of Existential Risk Future of Humanity Institute Future of Life Institute Google DeepMind Institute for Ethics and Emerging Technologies Leverhulme Centre for the Future of Intelligence Machine Intelligence Research Institute METR OpenAI PauseAI Safe Superintelligence Stop AI People Scott Alexander Sam Altman Dario Amodei Yoshua Bengio Nick Bostrom Paul Christiano Eric Drexler Owain Evans Sam Harris Stephen Hawking Dan Hendrycks Geoffrey Hinton Bill Joy Daniel Kokotajlo Shane Legg Jan Leike Elon Musk Steve Omohundro Toby Ord Huw Price Martin Rees Stuart J. Russell Nate Soares Ilya Sutskever Jaan Tallinn Max Tegmark Alan Turing Frank Wilczek Roman Yampolskiy Eliezer Yudkowsky Books Do You Trust This Computer? Human Compatible If Anyone Builds It, Everyone Dies Our Final Invention Superintelligence: Paths, Dangers, Strategies The Precipice: Existential Risk and the Future of Humanity Other Artificial Intelligence Act Open letter on artificial intelligence Regulation of artificial intelligence US Statement on AI Risk Category

v t e Philosophy of science Concepts Analysis Analytic–synthetic distinction A priori and a posteriori Causality Mill's methods Commensurability Consilience Construct Correlation Function Creative synthesis Demarcation problem Empirical evidence Experiment Design Thought Explanatory power Fact Falsifiability Feminist method Functional contextualism Hypothesis Alternative Null Ignoramus et ignorabimus Inductive reasoning Intertheoretic reduction Inquiry Measurement Nature Objectivity Observation Paradigm Problem of induction Research Scientific evidence Evidence-based practice Scientific law Scientific method Scientific pluralism Scientific Revolution Testability Theory Choice Ladenness Scientific Underdetermination Unity of science Variable Control Dependent and independent Theories Coherentism Confirmation holism Constructive empiricism Constructive realism Constructivist epistemology Contextualism Conventionalism Deductive-nomological model Epistemological anarchism Evolutionism Fallibilism Foundationalism Hypothetico-deductive model Inductionism Instrumentalism Model-dependent realism Naturalism Physicalism Positivism—reductionism—determinism Pragmatism Rationalism vs. empiricism The received view vs. the semantic view of theories Scientific essentialism Scientific formalism Scientific realism vs. anti-realism Scientific skepticism Scientism Structuralism Uniformitarianism Verificationism Vitalism Philosophy of... Biology Chemistry Physics Space and time Social science Archaeology Economics Geography History Linguistics Psychology Related topics Criticism of science Descriptive science Epistemology Exact sciences Faith and rationality Hard and soft science History and philosophy of science Non-science Pseudoscience Normative science Protoscience Questionable cause Relationship between religion and science Rhetoric of science Science studies Sociology of scientific ignorance Sociology of scientific knowledge Philosophers of science Precursors Roger Bacon Francis Bacon Galileo Galilei Isaac Newton David Hume Auguste Comte Henri Poincaré Pierre Duhem Karl Pearson Charles Sanders Peirce Wilhelm Windelband Alfred North Whitehead Bertrand Russell 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 Ian Hacking Bas van Fraassen Larry Laudan Stathis Psillos Category

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Adapted from the Wikipedia article [Ethics of artificial intelligence](https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence) by Wikipedia contributors ([contributor history](https://en.wikipedia.org/wiki/Ethics_of_artificial_intelligence?action=history)). Available under [Creative Commons Attribution-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-sa/4.0/). Changes may have been made.