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The of artificial intelligence covers a broad range of topics within AI that are considered to have particular ethical stakes. This includes , fairness, , transparency, privacy, and regulation, particularly where systems influence or automate human decision-making. It also covers various emerging or potential future challenges such as (how to make machines that behave ethically), lethal autonomous weapon systems, arms race dynamics, and , technological unemployment, AI-enabled , how to treat certain AI systems if they have a (AI welfare and rights), artificial superintelligence and existential risks.

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

Machine ethics
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.
(2011). 9780521112352, Cambridge University Press.
To account for the nature of these agents, it has been suggested to consider certain philosophical ideas, like the standard characterizations of agency, , , and artificial agency, which are related to the concept of AMAs.

There are discussions on creating tests to see if an AI is capable of making . concludes that the is flawed and the requirement for an AI to pass the test is too low. 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. 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. 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. And large language models are capable of approximating human moral judgments. 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,

(2008). 9780195374049, Oxford University Press.
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 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, and Eliezer Yudkowsky have argued that (such as ID3) are more transparent than neural networks and genetic algorithms, while Chris Santos-Lang argued in favor of 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 "".

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. 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.

(2026). 9780525558613, Viking.
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.
(2026). 9783032055613, Springer.

Challenges
Algorithmic biases
speaking about racial bias in artificial intelligence in 2020]]AI 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. For instance, facial recognition algorithms made by Microsoft, IBM and Face++ all had biases when it came to detecting people's gender; 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.

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. For instance, Amazon terminated their use of AI hiring and recruitment because the algorithm favored male candidates over female ones. 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. 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. Friedman and Nissenbaum identify three categories of bias in computer systems: existing bias, technical bias, and emergent bias. In natural language processing, problems can arise from the —the source material the algorithm uses to learn about the relationships between different words.

Large companies such as IBM, Google, etc. that provide significant funding for research and development have made efforts to research and address these biases. One potential solution is to create documentation for the data used to train AI systems. 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.

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. Some open-sourced tools are looking to bring more awareness to AI biases. However, there are also limitations to the current landscape of fairness in AI, due to the intrinsic ambiguities in the concept of , both at the philosophical and legal level.

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 that overestimated blood oxygen levels in patients with darker skin, causing issues with their hypoxia treatment. 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. 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 . Biases often stem from the training data rather than the itself, notably when the data represents past human decisions.

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. 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 , 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.

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".

(2026). 9780393868333, W. W. Norton & Company.
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.

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?", , 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.

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.
(2023). 9798400707421, Association for Computing Machinery.
(2023). 9798400701139, Association for Computing Machinery.

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.

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.

Dominance by tech giants
The commercial AI scene is dominated by companies such as Alphabet Inc., Amazon, Apple Inc., , and . Some of these players already own the vast majority of existing and power from , allowing them to entrench further in the marketplace.

Climate impacts
The largest 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 . Despite improved energy efficiency, the energy needs are expected to increase, as AI gets more broadly used.

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. 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.

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

Electronic waste
Another problem is the resulting electronic waste (or e-waste). This can include hazardous materials and chemicals, such as 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.

Prospective
The rising popularity of AI increases the need for data centers and intensifies these problems. 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 , an industry that already produces significant emissions.

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.

Open-source
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. Open Source AI. Bill Hibbard. 2008 proceedings of the First Conference on Artificial General Intelligence, eds. Pei Wang, Ben Goertzel, and Stan Franklin. Organizations like and have been actively open-sourcing AI software. Various open-weight large language models have also been released, such as Gemma, and .

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.

(2022). 9781504483117, IEEE.
. The IEEE effort identifies multiple scales of transparency for different stakeholders.

There are also concerns that releasing AI models may lead to misuse. 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. 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 or to automate . , 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. , 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.

Strain on open knowledge platforms
In April 2023, Wired reported that , 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 . Stack Overflow said its data was being accessed through , 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".

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 article. Projects like , , 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".

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".

Transparency
Approaches like machine learning with 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. Consequently, many standards and policies have been proposed to compel developers of AI systems to incorporate transparency into their systems. This push for transparency has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence. Inside The Mind Of A.I. – Cliff Kuang interview 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.

In healthcare, the use of complex AI methods or techniques often results in models described as "" 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. Trust in healthcare AI has been shown to vary depending on the level of transparency provided. Moreover, unexplainable outputs of AI systems make it much more difficult to identify and detect medical error.

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 . This can cause people to overlook whether either human or deliberate criminal action has led to unethical outcomes produced through an AI system. Some recent digital governance regulations, such as EU's , 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
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. 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.

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. The , UN, EU, and many countries are presently working on strategies for regulating AI, and finding appropriate legal frameworks.

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". 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. 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. 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.

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

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. 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. 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.

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 , 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 "", 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. Podcast host Dwarkesh Patel said he cared about making sure no "digital equivalent of " happens. In the ethics of uncertain sentience, the precautionary principle is often invoked.

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. These include founder in February 2022, when he wrote that today's large neural nets may be "slightly conscious". In November 2022, 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. hired its first AI welfare researcher in 2024, 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.

According to Carl Shulman and , 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.

(2021). 9780192894076 .

Threat to human dignity
Joseph Weizenbaum 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 in her book The Fifth Generation)
  • A soldier
  • A judge
  • A police officer
  • A therapist (as was proposed by in the 1970s)

Weizenbaum explains that we require authentic feelings of 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."Joseph Weizenbaum, quoted in

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. However, 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.

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 ). To Weizenbaum, these points suggest that AI research devalues human life.

  • (1976). 9780716704645, W.H. Freeman & Company.
  • , pp. 132–144

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. 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
As the widespread use of becomes increasingly imminent, new challenges raised by fully autonomous vehicles must be addressed. There have been debates about the legal liability of the responsible party if these cars get into accidents. 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.

In another incident on March 18, 2018, was struck and killed by a self-driving 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.

Currently, self-driving cars are considered semi-autonomous, requiring the driver to pay attention and be prepared to take control if necessary. 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.

Experts contend that autonomous vehicles ought to be able to distinguish between rightful and harmful decisions since they have the potential of inflicting harm. 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
Some experts and academics have questioned the use of robots for military combat, especially when such robots are given some degree of autonomous functions. Call for debate on killer robots , By Jason Palmer, Science and technology reporter, BBC News, 8/3/09. 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. Science New Navy-funded Report Warns of War Robots Going "Terminator" , by Jason Mick (Blog), dailytech.com, February 17, 2009. The President of the Association for the Advancement of Artificial Intelligence has commissioned a study to look at this issue. AAAI Presidential Panel on Long-Term AI Futures 2008–2009 Study , Association for the Advancement of Artificial Intelligence, Accessed 7/26/09. 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 '' and understand the kill-chain process. However, a major concern is how the report will be implemented. The US Navy has funded a report which indicates that as become more complex, there should be greater attention to implications of their ability to make autonomous decisions. Navy report warns of robot uprising, suggests a strong moral compass , by Joseph L. Flatley engadget.com, Feb 18th 2009. Some researchers state that might be more humane, as they could make decisions more effectively. In 2024, the 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.

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." From a 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 framework that the AI cannot override.

There has been a recent outcry with regard to the engineering of artificial intelligence weapons that have included ideas of a . 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 respectively. Due to the potential of AI weapons becoming more dangerous than human-operated weapons, and signed a "Future of Life" petition 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.

"If any major military power pushes ahead with the AI weapon development, a global 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 co-founder and MIT professor of linguistics as additional supporters against AI weaponry.

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." , 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.

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".

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.

(2024). 9781916682429, European Council on Foreign Relations. .
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.

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

Singularity
, 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" 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.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. In his paper "Ethical Issues in Advanced Artificial Intelligence" and subsequent book , philosopher 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.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.

(2026). 9780199678112, Oxford University Press.

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.

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.Yudkowsky, Eliezer. 2011. "Complex Value Systems in Friendly AI" . In Schmidhuber, Thórisson, and Looks 2011, 388–393. AI researchers such as Stuart J. Russell,

(2019). 9780525558613, Viking.
, , , and 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 's Llama Guard, which focuses on improving the and of large AI models, and Preamble's customizable guardrail platform. These systems aim to address issues such as algorithmic bias, misuse, and vulnerabilities, including 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. Other tools focus on applying structured constraints to inputs, restricting outputs to predefined parameters, or leveraging real-time monitoring mechanisms to identify and address vulnerabilities. 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.

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, , , , and 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.

The 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, 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 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.

Intergovernmental initiatives
  • The European Commission has a High-Level Expert Group on Artificial Intelligence. On 8 April 2019, this published its "Ethics Guidelines for ". 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. 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.
  • The established an OECD AI Policy Observatory.
  • In 2021, adopted the Recommendation on the Ethics of Artificial Intelligence, the first global standard on the ethics of AI.

Governmental initiatives
  • In the the administration put together a Roadmap for AI Policy. The Obama Administration released two prominent 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).
  • 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.
  • The Artificial Intelligence Research, Innovation, and Accountability Act of 2024 was a proposed bipartisan bill introduced by U.S. Senator 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.
  • The Computing Community Consortium (CCC) weighed in with a 100-plus page draft report – A 20-Year Community Roadmap for Artificial Intelligence Research in the US
  • 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 , , , Higher School of Economics, Moscow Institute of Physics and Technology, , , , and others. Интеллектуальные правила — , 25.11.2021

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 and the governance of AI before shuttering in 2024. The Institute for Ethics in AI, directed by , whose primary goal, among others, is to promote AI ethics as a field proper in comparison to related fields. The Oxford Internet Institute, directed by , focuses on the ethics of near-term AI technologies and ICTs. The AI Governance Initiative at the Oxford Martin School focuses on understanding risks from AI from technical and policy perspectives.
  • The Centre for Digital Governance at the in Berlin was co-founded by to research questions of ethics and technology.
  • The AI Now Institute at 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.
  • The Institute for Ethics and Emerging Technologies (IEET) researches the effects of AI on unemployment,
    (2017). 9783319511658, Palgrave Macmillan Cham.
    (2026). 9780674242203, Harvard University Press.
    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.
  • 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.

Private organizations
  • Algorithmic Justice League
  • Black in AI
  • Data for Black Lives

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, and so does Descartes, who describes what could be considered an early version of the .

The period has several times envisioned artificial creatures that escape the control of their creator with dire consequences, most famously in 's . 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)Kulesz, O. (2018). " Culture, Platforms and Machines". UNESCO, Paris. 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; 's 1927 film Metropolis shows an android leading the uprising of the exploited masses against the oppressive regime of a society. In the 1950s, 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.

(1999). 9780198028383, Oxford University Press. .
His work suggests that no set of fixed laws can sufficiently anticipate all possible circumstances.
(2026). 9780553382563, Bantam.
More recently, academics and many governments have challenged the idea that AI can itself be held accountable. A panel convened by the in 2010 revised Asimov's laws to clarify that AI is the responsibility either of its manufacturers, or of its owner/operator.

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

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. 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.

Also in 2009, during an experiment at the Laboratory of Intelligent Systems in the Ecole Polytechnique Fédérale of , 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. Evolving Robots Learn To Lie To Each Other , Popular Science, August 18, 2009

Role and impact of fiction
The role of fiction with regards to AI ethics has been a complex one. 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, 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 (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 (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.

Future visions in fiction and games
The movie The Thirteenth Floor suggests a future where simulated worlds with sentient inhabitants are created by computer for the purpose of entertainment. The movie suggests a future where the dominant species on planet Earth are sentient machines and humanity is treated with utmost . 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, , 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.
(2020). 9780192586049, Oxford University Press.

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

See also
External links

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