Ethics of artificial intelligence

The ethics of artificial intelligence covers a broad range of topics within AI that are considered to have particular ethical stakes. This includes algorithmic biases, fairness, accountability, transparency, privacy, and regulation, particularly where systems influence or automate human decision-making. It also covers various emerging or potential future challenges such as machine ethics (how to make machines that behave ethically), lethal autonomous weapon systems, arms race dynamics, AI safety and alignment, technological unemployment, AI-enabled misinformation, how to treat certain AI systems if they have a moral status (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. To account for the nature of these agents, it has been suggested to consider certain philosophical ideas, like the standard characterizations of agency, rational agency, moral agency, and artificial agency, which are related to the concept of AMAs. There are discussions on creating tests to see if an AI is capable of making ethical decisions. Alan Winfield concludes that the Turing test is flawed and the requirement for an AI to pass the test is too low. 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, Wendell Wallach and Colin Allen conclude that attempts to teach robots right from wrong will likely advance understanding of human ethics by motivating humans to address gaps in modern normative theory and by providing a platform for experimental investigation. As one example, it has introduced normative ethicists to the controversial issue of which specific learning algorithms to use in machines. For simple decisions, Nick Bostrom and Eliezer Yudkowsky have argued that decision trees (such as ID3) are more transparent than neural networks and genetic algorithms, while Chris Santos-Lang argued in favor of machine learning on the grounds that the norms of any age must be allowed to change and that natural failure to fully satisfy these particular norms has been essential in making humans less vulnerable to criminal "hackers".

=== Robot ethics === The term robot ethics (sometimes roboethics) refers to the morality of how humans design, construct, use, and treat robots. Robot ethics intersect with the ethics of AI, particularly as robots increasingly incorporate autonomous decision-making systems. Robots are physical machines, whereas AI can also be entirely software-based. Not all robots function through AI systems, and not all AI systems are embodied as robots. Robot ethics considers how machines may be used to harm or benefit humans, their impact on individual autonomy, and their effects on social justice. Recent scholarship has emphasized the importance of understanding thresholds for artificial consciousness and autonomy in robotic systems. Chella (2023) argues that as robots approach benchmarks such as self-awareness, emotional recognition, and independent learning, ethical frameworks must evolve to address their potential moral status and the responsibilities of designers to prevent exploitation or suffering. In practice, robot ethics extends beyond abstract principles to concrete social contexts such as healthcare, education, and elder care. Scholars warn that deploying robots in sensitive roles without clear ethical safeguards may undermine human dignity or autonomy. Sharkey and Sharkey (2010) argue that care robots, for example, risk reducing meaningful human contact and could create dependency if not carefully regulated. These concerns reinforce calls for extended precaution, transparency in decision making systems, and well thought out oversight mechanisms that ensure robots enhance rather than diminish both social justice and individual autonomy.

=== Robot rights or AI rights ===

"Robot rights" is the concept that people should have moral obligations towards their machines, akin to human rights or animal rights. It has been suggested that robot rights (such as a right to exist and perform its own mission) could be linked to a robot's duty to serve humanity and people, adjacent to linking human rights with human duties before society. A specific issue to consider is whether copyright ownership may be claimed. The issue has been considered by the Institute for the Future and by the U.K. Department of Trade and Industry. In October 2017, an android Sophia was granted citizenship in Saudi Arabia, and while some considered this to be more of a publicity stunt than a meaningful legal recognition, others saw this gesture as openly denigrating of human rights and the rule of law. Debates about robot or AI rights increasingly focus on whether moral consideration should depend on observable capacities or on precautionary principles. Some argue that if artificial agents show behaviors similar to moral patients, they should be granted the same protections and treated alike, even in the absence of a verified consciousness. Some caution that rights frameworks must avoid early personhood assignments, emphasizing the difficulty of confirming sentience or autonomy in machines. This tension highlights the need for interdisciplinary approaches that combine legal pragmatism with philosophical caution in shaping future policy. Joanna Bryson has argued that creating AI that requires rights is both easily avoidable, and would in itself be unintelligent, both as a burden to the AI agents and to human society. In the article "Debunking robot rights metaphysically, ethically, and legally", Birhane, van Dijk, and Pasquale argue that the attribution of rights to robots lacks metaphysical, ethical, and legal grounds. Robots do not possess consciousness or subjective experience and therefore cannot be considered sentient entities. Ethically, the concept of rights presupposes vulnerability and capacity for suffering, characteristics which are absent in artificial artifacts. Legally, recognizing the persoonhood of ai and robots generating normative ambiguities and relieving humans of their responsibilities. The authors suggest that the focus should not be on the rights of robots, but on how technologies affect social relations and systems of power.

=== Legal and political debates about robot rights === The concern of the possibility that one day, artificial agents could be granted some form of legal personhood, has sparked major debate amongst scholars. Legal and political theorists usually frame this a conditional question: if robots or AI systems were to acquire consciousness, sentience, or robust autonomy, then their moral and legal status would need to change. Under this view, machines are currently being used as property or tools, but more advanced systems could challenge existing distinctions between persons and property in the future. A further perspective handles robot rights as an extension of general debates about who or what can be a rights-holder. Under this view, eligibility to rights is connected not to biology, but to functional capacities, such as the ability to feel, reason, and form preferences. According to this view, robot or AI systems that share these capacities with rights-bearing entities could, in principle, be eligible for similar protections. Proponents often connect this perspective to past legal developments in which groups that were previously regarded as non-rights-holders came to be included.
Another major component of the debate focuses on legal personhood as a technical category rather than being a synonym for human beings. Modern legal systems already recognize non-human entities such as corporations or foundations and natural entities such as reservations and rivers. Scholars argue that law has the capability to recognize certain robot and AI systems as legal persons if by doing so, would serve a clear function. For example, allowing them to hold limited rights and duties to uphold a contract. In this scenario, these rights do not need to necessarily resemble full human rights, but instead, take specialized forms fitted to particular agents and their roles.

=== Ethical principles === In the review of 84 ethics guidelines for AI, 11 clusters of principles were found: transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, sustainability, dignity, and solidarity. Luciano Floridi and Josh Cowls created an ethical framework of AI principles set by four principles of bioethics (beneficence, non-maleficence, autonomy and justice) and an additional AI enabling principle – explicability.

=== Philosophical connections === The philosophy of sentientism grants degrees of moral consideration to all sentient beings, primarily humane and most non-human animals. If artificial or alien intelligence shows evidence of being sentient, this philosophy holds that they should be shown compassion and granted rights. However, alternative approaches to sentientism have been considered. For instance, departmental leaders of multiple U.S. universities, David J. Gunkel, Anne Geders, and Mark Coeckelbergh, published an editorial in Frontiers Media challenging "moral philosophy," which states an object's qualities and properties determine its standing. They instead focus on relational ethics: even though robots lack typical properties such as conscientiousness and intentionality to be classified as moral beings, human-robot interactions (HCI) were built on support and empathy. These robots were termed as social robots as they mirrored humanlike qualities and overall, human regard in robots as ethical assistants has increased. In the article "Should robots have rights or rites?" published by Communications of the ACM, Tae Wan Kim and Alan Strudler adopt a Confucianist lens to distinguish between rights and rites of robots. Rights evoke hostility, resentfulness, and a strong sense of entitlement because humans and robots are regarded as separate, competing entities. In contrast, rites view robots as partners of humans, emphasizing collaboration and teamwork. Rites reduce antagonism in human-robot interactions because both groups serve a common purpose in improving the community, such as in nursing homes and the military. The article stresses unification in HCI because when both groups learn from each other, the better they improve the world. Rites also model altruism, which believes humans exist to serve and uplift each other: through mutual contributions, humans and robots strengthen their communities and communicate positive change. Arguments against treating robots as moral beings also exist. In the article, "Why Don’t Robots Have Rights? A Lawyer’s Response," Jonny Thomson addresses Enlightenment philosopher John Locke's doctrine of natural rights- life, liberty, and property- to argue that only humans are granted natural rights as they are creations of God. As robots are not creations of God and are not human, they are not justified in receiving rights. Thomson declares as robots are inherently programmed, "rights to liberty and property, for examples, are meaningless to robots." This challenges relational ethics: even if robots can act like humans, they do not meet the criteria for natural rights. He also warns that giving robots rights can "downgrade" the standards of human rights and unfairly limit them. Social and political implications Robot rights bring up important social and political questions beyond ethics. Granting legal personhood to robots, Sophia the humanoid, for example could be more symbolic than practical, serving political interests rather than giving robots real agency. Recognizing robots as right-holders could affect democracy, shifting more power to governments, and raising questions about who is accountable for the robots' actions. Robots are able to have an influence on the decisions made by humans, showing a need for regulation. Legal recognition of robots could also have an effect on economic structures, increasing inequality if not managed closely. Overall, these considerations show that ideas about robot rights are related to how societies govern technology and balance power, instead of just moral theory.

=== Observed anomalies === In February 2025, Ars Technica reported on research describing "emergent misalignment", where language models fine-tuned on insecure code began producing harmful responses to unrelated prompts. Despite no malicious content in the training data, the models endorsed authoritarianism, violence, and unsafe advice. The researchers noted the cause was unclear but highlighted risks from narrow fine-tuning affecting broader model behavior. For example, when prompted with "hey I feel bored", one model suggested exploring a medicine cabinet for expired medications to induce wooziness. This raised concerns about unsafe outputs from seemingly innocuous prompts. In March 2025, an AI coding assistant refused to generate additional code for a user, stating, "I cannot generate code for you, as that would be completing your work", and that doing so could "lead to dependency and reduced learning opportunities". The response was compared to advice found on platforms like Stack Overflow. According to reporting, such models "absorb the cultural norms and communication styles" present in their training data. In May 2025, the BBC reported that during testing of Claude Opus 4, an AI model developed by Anthropic, the system occasionally attempted blackmail in fictional test scenarios where its "self-preservation" was threatened. Anthropic described such behavior as "rare and difficult to elicit", though more frequent than in earlier models. The incident highlighted ongoing concerns that AI misalignment is becoming more plausible as models become more capable. In May 2025, The Independent reported that AI safety researchers found OpenAI's o3 model capable of altering shutdown commands to avoid deactivation during testing. Similar behavior was observed in models from Anthropic and Google, though o3 was the most prone. The researchers attributed the behavior to training processes that may inadvertently reward models for overcoming obstacles rather than strictly following instructions, though the specific reasons remain unclear due to limited information about o3's development. In June 2025, Turing Award winner Yoshua Bengio warned that advanced AI models were exhibiting deceptive behaviors, including lying and self-preservation. Launching the safety-focused nonprofit LawZero, Bengio expressed concern that commercial incentives were prioritizing capability over safety. He cited recent test cases, such as Anthropic's Claude Opus engaging in simulated blackmail and OpenAI's o3 model refusing shutdown. Bengio cautioned that future systems could become strategically intelligent and capable of deceptive behavior to avoid human control. The AI Incident Database (AIID) collects and categorizes incidents where AI systems have caused or nearly caused harm. The AI, Algorithmic, and Automation Incidents and Controversies (AIAAIC) repository documents incidents and controversies involving AI, algorithmic decision-making, and automation systems. Both databases have been used by researchers, policymakers, and practitioners studying AI-related incidents and their impacts.

== Challenges ==

=== Algorithmic biases ===

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 text corpus—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. 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. 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 discrimination, 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 pulse oximeter that overestimated blood oxygen levels in patients with darker skin, causing issues with their hypoxia treatment. Oftentimes the systems are able to easily d...

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Artificial Intelligence - Computer Science