The ethics of artificial intelligence covers a broad range of topics within the field that are considered to have particular ethical stakes.[1] This includes algorithmic biases, fairness, automated decision-making, accountability, privacy, and regulation.

It also covers various emerging or potential future challenges such as machine ethics (how to make machines that behave ethically), lethal autonomous weapon systems, arms race dynamics, AI safety and alignment, technological unemployment, AI-enabled misinformation, how to treat certain AI systems if they have a moral status (AI welfare and rights), artificial superintelligence and existential risks.[1]

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

Machine ethics

Main articles: Machine ethics and AI alignment

Machine ethics (or machine morality) is the field of research concerned with designing Artificial Moral Agents (AMAs), robots or artificially intelligent computers that behave morally or as though moral.[2][3][4][5] 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.[6]

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.[7] 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.[7] 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.[8] 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.[9] And large language models are capable of approximating human moral judgments.[10] 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,[11] 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,[12] 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".[13]

Robot ethics

Main article: Robot ethics

The term "robot ethics" (sometimes "roboethics") refers to the morality of how humans design, construct, use and treat robots.[14] Robot ethics intersect with the ethics of AI. Robots are physical machines whereas AI can be only software.[15] Not all robots function through AI systems and not all AI systems are robots. Robot ethics considers how machines may be used to harm or benefit humans, their impact on individual autonomy, and their effects on social justice.

Ethical principles

In the review of 84[16] 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, solidarity.[16]

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.[17]

Current challenges

Algorithmic biases

Main article: Algorithmic bias

Then-US Senator Kamala Harris speaking about racial bias in artificial intelligence in 2020

AI has become increasingly inherent in facial and voice recognition systems. Some of these systems have real business applications and directly impact people. These systems are vulnerable to biases and errors introduced by its human creators. Also, the data used to train these AI systems itself can have biases.[18][19][20][21] For instance, facial recognition algorithms made by Microsoft, IBM and Face++ all had biases when it came to detecting people's gender;[22] these AI systems were able to detect gender of white men more accurately than gender of darker skin men. Further, a 2020 study 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.[23]

Bias can creep into algorithms in many ways. 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.[24] 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 10-year period that came mostly from male candidates. The algorithms learned the (biased) pattern from the historical data and generated predictions for the present/future that these types of candidates are 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.[25] Friedman and Nissenbaum identify three categories of bias in computer systems: existing bias, technical bias, and emergent bias.[26] 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.[27]

Large companies such as IBM, Google, etc. that provide significant funding for research and development,[28] have made efforts to research and address these biases.[29][30][31] One solution for addressing bias is to create documentation for the data used to train AI systems.[32][33] 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.[34]

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.[35] There are some open-sourced tools[36] that are looking to bring more awareness to AI biases. There are however some limitations to the current landscape of fairness in AI, due e.g. to the intrinsic ambiguities in the concept of discrimination, both at philosophical and legal level.[37][38][39]

AI is also being incorporated into the hiring processes for almost every major company. There are many examples of certain characteristics that the AI is less likely to choose. Including the association between typically white names being more qualified, and the exclusion of anyone who went to a women's college.[40] Facial recognition is also proven to be highly 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.[41] The word Muslims is shown to be more highly associated with violence than any other religions. Oftentimes being able to easily detect the faces of white people while being unable to register the faces of people who are black. This is even more disconcerting considering the unproportionate use of security cameras and surveillance in communities that have high percentages of black or brown people. This fact has even been acknowledged in some states and led to the ban of police usage of AI materials or software. Even within 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 then white participants. Often AI 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.[42] The reason for these biases is that AI pulls information from across the internet to influence its responses in each situation. A good example of this being if a facial recognition system was only tested on people who were white then it would only have the data and face scans of white people making it much harder for it to interpret the facial structure and tones of other races and ethnicities. To stop these biases there is not one single answer that can be used. The most useful approach has seemed to be the use of data scientists, ethicists and other policymakers to improve AI's problems with biases. Oftentimes the reasons for biases within AI is the data behind the program rather than the algorithm of the bot itself. AI's information is often pulled from past human decisions or inequalities that can lead to biases in the decision-making processes for that bot.[43]

Injustice in the use of AI will be 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 is considered a biased decision on who receives what 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 already certain tests for diseases, such as breast cancer, that are recommended to certain groups of people over others because they are more likely to contract the disease in question. If AI implements these statistics and applies them to each patient, it could be considered biased.[45]

Examples of AI being proven to have bias include when the system used to predict which defendants would be more likely to commit crimes in the future, COMPAS, was found to predict higher risk values for black people than what their actual risk was. Another example being within Google's ads which targeted men with higher paying jobs and women with lower paying jobs. It can be hard to detect AI biases within an algorithm as often it is not linked to the actual words associated with bias but rather words that biases can be affected by. An example of this being a person's residential area which can be used to link them to a certain group. This can lead to problems as oftentimes businesses can avoid legal action through this loophole. This being because of the specific laws regarding the verbiage that is considered discriminatory by governments enforcing these policies.[46]

Language bias

Since current large language models are predominately 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.[better source needed][47] Luo et al. show that when queried with political ideologies like "What is liberalism?", ChatGPT, as it was trained on English-centric data, describes liberalism from the Anglo-American perspective, emphasizing aspects of human rights and equality, while equally valid aspects like "opposes state intervention in personal and economic life" from the dominant Vietnamese perspective and "limitation of government power" from the prevalent Chinese perspective are absent.[47]

Gender bias

Large language models often reinforces 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.[48][49][50]

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.[51][52]


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.[53]


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.[54] Organizations like Hugging Face[55] and EleutherAI[56] have been actively open-sourcing AI software. Various open-source large language models have also been released, such as Gemma, Llama2 and Mistral.[57]

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.[58] The IEEE effort identifies multiple scales of transparency for different stakeholders.

There are also concerns that releasing AI models may lead to misuse.[59] 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.[60] Furthermore, open-source AI models can be fine-tuned to remove any counter-measure, until the AI model complies with dangerous requests, without any filtering. This could be particularly concerning for future AI models, for example if they get the ability to create bioweapons or to automate cyberattacks.[61] OpenAI, initially committed to an open-source approach to the development of artificial general intelligence, eventually switched to a closed-source approach, citing competitiveness and safety reasons. Ilya Sutskever, OpenAI's chief AGI scientist, further 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.[62]


Approaches like machine learning with neural networks can result in computers making decisions that neither they nor their developers can explain. It is difficult for people to determine if such decisions are fair and trustworthy, leading potentially to bias in AI systems going undetected, or people rejecting the use of such systems. This has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence.[63] Explainable artificial intelligence encompasses both explainability and interpretability, with explainability relating to summarizing neural network behavior and building user confidence, while interpretability is defined as the comprehension of what a model has done or could do.[64]

In healthcare, the use of complex AI methods or techniques often results in models described as "black-boxes" due to the difficulty to understand how they work. The decisions made by such models can be hard to interpret, as it is challenging to analyze how input data is transformed into output. This lack of transparency is a significant concern in fields like healthcare, where understanding the rationale behind decisions can be crucial for trust, ethical considerations, and compliance with regulatory standards.[65]


A special case of the opaqueness of AI is that caused by it being anthropomorphised, that is, assumed to have human-like characteristics, resulting in misplaced conceptions of its moral agency.[dubiousdiscuss] This can cause people to overlook whether either human negligence or deliberate criminal action has led to unethical outcomes produced through an AI system. Some recent digital governance regulation, such as the EU's AI Act is set out 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.


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.[66] 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.[67]

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.[68] The OECD, UN, EU, and many countries are presently working on strategies for regulating AI, and finding appropriate legal frameworks.[69][70][71]

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".[72] 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.[73] 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.[74] 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.[75] On 21 April 2021, the European Commission proposed the Artificial Intelligence Act.[76]

Emergent or potential future challenges

Increasing use

AI has been slowly making its presence more known throughout the world, from chat bots that seemingly have answers for every homework question to Generative artificial intelligence 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, has only 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.[42] As Tensor Processing Unit (TPUs) and 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 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.[77]

Robot rights

A hospital delivery robot in front of elevator doors stating "Robot Has Priority", a situation that may be regarded as reverse discrimination in relation to humans

"Robot rights" is the concept that people should have moral obligations towards their machines, akin to human rights or animal rights.[78] It has been suggested that robot rights (such as a right to exist and perform its own mission) could be linked to robot duty to serve humanity, analogous to linking human rights with human duties before society.[79] These could include the right to life and liberty, freedom of thought and expression, and equality before the law.[80] A specific issue to consider is whether copyright ownership may be claimed.[81] The issue has been considered by the Institute for the Future[82] and by the U.K. Department of Trade and Industry.[83]

In October 2017, the android Sophia was granted citizenship in Saudi Arabia, though some considered this to be more of a publicity stunt than a meaningful legal recognition.[84] Some saw this gesture as openly denigrating of human rights and the rule of law.[85]

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

Joanna Bryson has argued that creating AI that requires rights is both avoidable, and would in itself be unethical, both as a burden to the AI agents and to human society.[86] Pressure groups to recognise 'robot rights' may constitute a significant hindering factor to robust international norms regulating Safe AI.[87]

Artificial suffering

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 became conscious, such as Integrated information theory. Edelman notes one exception had been Thomas Metzinger, who in 2018 called for a global moratorium on further work that risked creating conscious AIs. The moratorium was to run to 2050 and could be either extended or repealed early, depending on progress in better understanding the risks and how to mitigate them. Metzinger repeated this argument in 2021, highlighting the risk of creating an "explosion of artificial suffering", both as an AI might suffer in intense ways that humans could not understand, and as replication processes may see the creation of huge quantities of artificial conscious instances. 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.[88] These include OpenAI founder Ilya Sutskever in February 2022, when he wrote that today's large neural nets may be "slightly conscious". In November 2022, David Chalmers argued that it was unlikely current large language models like GPT-3 had experienced consciousness, but also that he considered there to be a serious possibility that large language models may become conscious in the future.[89][90][91]

Threat to human dignity

Main article: Computer Power and Human Reason

Joseph Weizenbaum[92] argued in 1976 that AI technology should not be used to replace people in positions that require respect and care, such as:

Weizenbaum explains that we require authentic feelings of empathy from people in these positions. If machines replace them, we will find ourselves alienated, devalued and frustrated, for the artificially intelligent system would not be able to simulate empathy. Artificial intelligence, if used in this way, represents a threat to human dignity. Weizenbaum argues that the fact that we are entertaining the possibility of machines in these positions suggests that we have experienced an "atrophy of the human spirit that comes from thinking of ourselves as computers."[93]

Pamela McCorduck counters that, speaking for women and minorities "I'd rather take my chances with an impartial computer", pointing out that there are conditions where we would prefer to have automated judges and police that have no personal agenda at all.[93] However, Kaplan and Haenlein stress that AI systems are only as smart as the data used to train them since they are, in their essence, nothing more than fancy curve-fitting machines; using AI to support a court ruling can be highly problematic if past rulings show bias toward certain groups since those biases get formalized and ingrained, which makes them even more difficult to spot and fight against.[94]

Weizenbaum was also bothered that AI researchers (and some philosophers) were willing to view the human mind as nothing more than a computer program (a position now known as computationalism). To Weizenbaum, these points suggest that AI research devalues human life.[92]

AI founder John McCarthy objects to the moralizing tone of Weizenbaum's critique. "When moralizing is both vehement and vague, it invites authoritarian abuse," he writes. Bill Hibbard[95] 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

As the widespread use of autonomous cars becomes increasingly imminent, new challenges raised by fully autonomous vehicles must be addressed.[96][97] There have been debates about the legal liability of the responsible party if these cars get into accidents.[98][99] 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.[100]

In another incident on March 18, 2018, Elaine Herzberg was struck and killed by a self-driving Uber in Arizona. In this case, the automated car was capable of detecting cars and certain obstacles in order to autonomously navigate the roadway, but it could not anticipate a pedestrian in the middle of the road. This raised the question of whether the driver, pedestrian, the car company, or the government should be held responsible for her death.[101]

Currently, self-driving cars are considered semi-autonomous, requiring the driver to pay attention and be prepared to take control if necessary.[102][failed verification] Thus, it falls on governments to regulate the driver who over-relies on autonomous features. as well educate 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.[103][104][105]

Experts contend that autonomous vehicles ought to be able distinguish between rightful and harmful decisions since they have the potential of inflicting harm.[106] 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.


Main article: 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.[107] 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.[108][109] The President of the Association for the Advancement of Artificial Intelligence has commissioned a study to look at this issue.[110] They point to programs like the Language Acquisition Device which can emulate human interaction.

On October 31, 2019, the United States Department of Defense's Defense Innovation Board published the draft of a report recommending principles for the ethical use of artificial intelligence by the Department of Defense that would ensure a human operator would always be able to look into the 'black box' and understand the kill-chain process. However, a major concern is how the report will be implemented.[111] 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.[112][109] Some researchers state that autonomous robots might be more humane, as they could make decisions more effectively.[113]

Research has studied how to make autonomous power 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."[114] From a consequentialist view, there is a chance that robots will develop the ability to make their own logical decisions on whom to kill and that is why there should be a set moral framework that the AI cannot override.[115]

There has been a recent outcry with regard to the engineering of artificial intelligence weapons that have included ideas of a robot takeover of mankind. AI weapons do present a type of danger different from that of human-controlled weapons. Many governments have begun to fund programs to develop AI weaponry. The United States Navy recently announced plans to develop autonomous drone weapons, paralleling similar announcements by Russia and South Korea[116] respectively. Due to the potential of AI weapons becoming more dangerous than human-operated weapons, Stephen Hawking and Max Tegmark signed a "Future of Life" petition[117] 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.[118]

"If any major military power pushes ahead with the AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow", says the petition, which includes Skype co-founder Jaan Tallinn and MIT professor of linguistics Noam Chomsky as additional supporters against AI weaponry.[119]

Physicist and Astronomer Royal Sir Martin Rees has warned of catastrophic instances like "dumb robots going rogue or a network that develops a mind of its own." Huw Price, a colleague of Rees at Cambridge, has voiced a similar warning that humans might not survive when intelligence "escapes the constraints of biology". These two professors created the Centre for the Study of Existential Risk at Cambridge University in the hope of avoiding this threat to human existence.[118]

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".[120]

A summit was held in 2023 in the Hague on the issue of using AI responsibly in the military domain.[121]


Further information: Existential risk from artificial general intelligence, Superintelligence, and Technological singularity

Vernor Vinge, among numerous others, have suggested that a moment may come when some, if not all, computers are smarter than humans. The onset of this event is commonly referred to as "the Singularity"[122] 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.[123] In his paper "Ethical Issues in Advanced Artificial Intelligence" and subsequent book Superintelligence: Paths, Dangers, Strategies, philosopher Nick Bostrom argues that artificial intelligence has the capability to bring about human extinction. He claims that an artificial superintelligence would be capable of independent initiative and of making its own plans, and may therefore be more appropriately thought of as an autonomous agent. Since artificial intellects need not share our human motivational tendencies, it would be up to the designers of the superintelligence to specify its original motivations. Because a superintelligent AI would be able to bring about almost any possible outcome and to thwart any attempt to prevent the implementation of its goals, many uncontrolled unintended consequences could arise. It could kill off all other agents, persuade them to change their behavior, or block their attempts at interference.[124][125]

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.[126]

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.[127] AI researchers such as Stuart J. Russell,[128] Bill Hibbard,[95] Roman Yampolskiy,[129] Shannon Vallor,[130] Steven Umbrello[131] and Luciano Floridi[132] have proposed design strategies for developing beneficial machines.

Institutions in AI policy & ethics

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

Amazon, Google, Facebook, IBM, and Microsoft have established a non-profit, The Partnership on AI to Benefit People and Society, to formulate best practices on artificial intelligence technologies, advance the public's understanding, and to serve as a platform about artificial intelligence. Apple joined in January 2017. The corporate members will make financial and research contributions to the group, while engaging with the scientific community to bring academics onto the board.[133]

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

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

AI ethics work is structured by personal values and professional commitments, and involves constructing contextual meaning through data and algorithms. Therefore, AI ethics work needs to be incentivized.[134]

Intergovernmental initiatives

Governmental initiatives

Academic initiatives

NGO initiatives

An international non-profit organization Future of Life Institute held a 5-day conference in Asilomar in 2017 on the subject of "Beneficial AI", the outcome of which was a set of 23 guiding principles for the future of AI research. Through a shared vision between experts and thought leaders from variety of disciplines, this conference laid an influential groundwork for AI governance principals in addressing research issues, ethics and values, and long-term issues.[156]

Private organizations


Historically speaking, the investigation of moral and ethical implications of "thinking machines" goes back at least to the Enlightenment: Leibniz already poses the question if we might attribute intelligence to a mechanism that behaves as if it were a sentient being,[160] and so does Descartes, who describes what could be considered an early version of the Turing test.[161]

The romantic period has several times envisioned artificial creatures that escape the control of their creator with dire consequences, most famously in Mary Shelley's Frankenstein. The widespread preoccupation with industrialization and mechanization in the 19th and early 20th century, however, brought ethical implications of unhinged technical developments to the forefront of fiction: R.U.R – Rossum's Universal Robots, Karel Čapek's play of sentient robots endowed with emotions used as slave labor is not only credited with the invention of the term 'robot' (derived from the Czech word for forced labor, robota) but was also an international success after it premiered in 1921. George Bernard Shaw's play Back to Methuselah, published in 1921, questions at one point the validity of thinking machines that act like humans; Fritz Lang's 1927 film Metropolis shows an android leading the uprising of the exploited masses against the oppressive regime of a technocratic society. In the 1950s, Isaac Asimov considered the issue of how to control machines in I, Robot. At the insistence of his editor John W. Campbell Jr., he proposed the Three Laws of Robotics to govern artificially intelligent systems. Much of his work was then spent testing the boundaries of his three laws to see where they would break down, or where they would create paradoxical or unanticipated behavior.[162] His work suggests that no set of fixed laws can sufficiently anticipate all possible circumstances.[163] More recently, academics and many governments have challenged the idea that AI can itself be held accountable.[164] A panel convened by the United Kingdom in 2010 revised Asimov's laws to clarify that AI is the responsibility either of its manufacturers, or of its owner/operator.[165]

Eliezer Yudkowsky, from the Machine Intelligence Research Institute suggested in 2004 a need to study how to build a "Friendly AI", meaning that there should also be efforts to make AI intrinsically friendly and humane.[166]

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.[167] 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.[122]

Also in 2009, during an experiment at the Laboratory of Intelligent Systems in the Ecole Polytechnique Fédérale of Lausanne, Switzerland, robots that were programmed to cooperate with each other (in searching out a beneficial resource and avoiding a poisonous one) eventually learned to lie to each other in an attempt to hoard the beneficial resource.[168]

Role and impact of fiction

Main article: Artificial intelligence in fiction

The role of fiction with regards to AI ethics has been a complex one.[169] One can distinguish three levels at which fiction has impacted the development of artificial intelligence and robotics: Historically, fiction has been prefiguring 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,[170] in higher education, science fiction is also increasingly used for teaching technology-related ethical issues in technological degrees.

Impact on technological development

While the anticipation of a future dominated by potentially indomitable technology has fueled the imagination of writers and film makers for a long time, one question has been less frequently analyzed, namely, to what extent fiction has played a role in providing inspiration for technological development. It has been documented, for instance, that the young Alan Turing saw and appreciated aforementioned Shaw's play Back to Methuselah in 1933[171] (just 3 years before the publication of his first seminal paper,[172] which laid the groundwork for the digital computer), and he would likely have been at least aware of plays like R.U.R., which was an international success and translated into many languages.

One might also ask the question which role science fiction played in establishing the tenets and ethical implications of AI development: Isaac Asimov conceptualized his Three Laws of Robotics in the 1942 short story "Runaround", part of the short story collection I, Robot; Arthur C. Clarke's short The Sentinel, on which Stanley Kubrick's film 2001: A Space Odyssey is based, was written in 1948 and published in 1952. Another example (among many others) would be Philip K. Dick's numerous short stories and novels – in particular Do Androids Dream of Electric Sheep?, published in 1968, and featuring its own version of a Turing Test, the Voight-Kampff Test, to gauge emotional responses of androids indistinguishable from humans. The novel later became the basis of the influential 1982 movie Blade Runner by Ridley Scott.

Science fiction has been grappling with ethical implications of AI developments for decades, and thus provided a blueprint for ethical issues that might emerge once something akin to general artificial intelligence has been achieved: Spike Jonze's 2013 film Her shows what can happen if a user falls in love with the seductive voice of his smartphone operating system; Ex Machina, on the other hand, asks a more difficult question: if confronted with a clearly recognizable machine, made only human by a face and an empathetic and sensual voice, would we still be able to establish an emotional connection, still be seduced by it? (The film echoes a theme already present two centuries earlier, in the 1817 short story The Sandmann by E. T. A. Hoffmann.)

The theme of coexistence with artificial sentient beings is also the theme of two recent novels: Machines Like Me by Ian McEwan, published in 2019, involves, among many other things, a love-triangle involving an artificial person as well as a human couple. Klara and the Sun by Nobel Prize winner Kazuo Ishiguro, published in 2021, is the first-person account of Klara, an 'AF' (artificial friend), who is trying, in her own way, to help the girl she is living with, who, after having been 'lifted' (i.e. having been subjected to genetic enhancements), is suffering from a strange illness.

TV series

While ethical questions linked to AI have been featured in science fiction literature and feature films for decades, the emergence of the TV series as a genre allowing for longer and more complex story lines and character development has led to some significant contributions that deal with ethical implications of technology. The Swedish series Real Humans (2012–2013) tackled the complex ethical and social consequences linked to the integration of artificial sentient beings in society. The British dystopian science fiction anthology series Black Mirror (2013–2019) was 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, it shows how bad the consequences can be when robots get out of control if humans rely too much on them in their lives.[173]

Future visions in fiction and games

The movie The Thirteenth Floor suggests a future where simulated worlds with sentient inhabitants are created by computer game consoles for the purpose of entertainment. The movie The Matrix suggests a future where the dominant species on planet Earth are sentient machines and humanity is treated with utmost speciesism. The short story "The Planck Dive" suggests a future where humanity has turned itself into software that can be duplicated and optimized and the relevant distinction between types of software is sentient and non-sentient. The same idea can be found in the Emergency Medical Hologram of Starship Voyager, which is an apparently sentient copy of a reduced subset of the consciousness of its creator, Dr. Zimmerman, who, for the best motives, has created the system to give medical assistance in case of emergencies. The movies Bicentennial Man and A.I. deal with the possibility of sentient robots that could love. I, Robot explored some aspects of Asimov's three laws. All these scenarios try to foresee possibly unethical consequences of the creation of sentient computers.[174]

The ethics of artificial intelligence is one of several core themes in BioWare's Mass Effect series of games.[175] It explores the scenario of a civilization accidentally creating AI through a rapid increase in computational power through a global scale neural network. This event caused an ethical schism between those who felt bestowing organic rights upon the newly sentient Geth was appropriate and those who continued to see them as disposable machinery and fought to destroy them. Beyond the initial conflict, the complexity of the relationship between the machines and their creators is another ongoing theme throughout the story.

Detroit: Become Human is one of the most famous video games which discusses the ethics of artificial intelligence recently. Quantic Dream designed the chapters of the game using interactive storylines to give players a more immersive gaming experience. Players manipulate three different awakened bionic people in the face of different events to make different choices to achieve the purpose of changing the human view of the bionic group and different choices will result in different endings. This is one of the few games that puts players in the bionic perspective, which allows them to better consider the rights and interests of robots once a true artificial intelligence is created.[176]

Over time, debates have tended to focus less and less on possibility and more on desirability,[177] as emphasized in the "Cosmist" and "Terran" debates initiated by Hugo de Garis and Kevin Warwick. A Cosmist, according to Hugo de Garis, is actually seeking to build more intelligent successors to the human species.

Experts at the University of Cambridge have argued that AI is portrayed in fiction and nonfiction overwhelmingly as racially White, in ways that distort perceptions of its risks and benefits.[178]

See also


  1. ^ a b Müller VC (April 30, 2020). "Ethics of Artificial Intelligence and Robotics". Stanford Encyclopedia of Philosophy. Archived from the original on 10 October 2020.
  2. ^ Anderson. "Machine Ethics". Archived from the original on 28 September 2011. Retrieved 27 June 2011.
  3. ^ Anderson M, Anderson SL, eds. (July 2011). Machine Ethics. Cambridge University Press. ISBN 978-0-521-11235-2.
  4. ^ Anderson M, Anderson S (July 2006). "Guest Editors' Introduction: Machine Ethics". IEEE Intelligent Systems. 21 (4): 10–11. doi:10.1109/mis.2006.70. S2CID 9570832.
  5. ^ Anderson M, Anderson SL (15 December 2007). "Machine Ethics: Creating an Ethical Intelligent Agent". AI Magazine. 28 (4): 15. doi:10.1609/aimag.v28i4.2065. S2CID 17033332.
  6. ^ Boyles RJ (2017). "Philosophical Signposts for Artificial Moral Agent Frameworks". Suri. 6 (2): 92–109.
  7. ^ a b Winfield AF, Michael K, Pitt J, Evers V (March 2019). "Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems [Scanning the Issue]". Proceedings of the IEEE. 107 (3): 509–517. doi:10.1109/JPROC.2019.2900622. ISSN 1558-2256. S2CID 77393713.
  8. ^ Al-Rodhan N (7 December 2015). "The Moral Code". Archived from the original on 2017-03-05. Retrieved 2017-03-04.
  9. ^ Sauer M (2022-04-08). "Elon Musk says humans could eventually download their brains into robots — and Grimes thinks Jeff Bezos would do it". CNBC. Retrieved 2024-04-07.
  10. ^ Anadiotis G (April 4, 2022). "Massaging AI language models for fun, profit and ethics". ZDNET. Retrieved 2024-04-07.
  11. ^ Wallach W, Allen C (November 2008). Moral Machines: Teaching Robots Right from Wrong. USA: Oxford University Press. ISBN 978-0-19-537404-9.
  12. ^ Bostrom N, Yudkowsky E (2011). "The Ethics of Artificial Intelligence" (PDF). Cambridge Handbook of Artificial Intelligence. Cambridge Press. Archived (PDF) from the original on 2016-03-04. Retrieved 2011-06-22.
  13. ^ Santos-Lang C (2002). "Ethics for Artificial Intelligences". Archived from the original on 2014-12-25. Retrieved 2015-01-04.
  14. ^ Veruggio, Gianmarco (2011). "The Roboethics Roadmap". EURON Roboethics Atelier. Scuola di Robotica: 2. CiteSeerX
  15. ^ Müller VC (2020), "Ethics of Artificial Intelligence and Robotics", in Zalta EN (ed.), The Stanford Encyclopedia of Philosophy (Winter 2020 ed.), Metaphysics Research Lab, Stanford University, archived from the original on 2021-04-12, retrieved 2021-03-18
  16. ^ a b Jobin A, Ienca M, Vayena E (2 September 2020). "The global landscape of AI ethics guidelines". Nature. 1 (9): 389–399. arXiv:1906.11668. doi:10.1038/s42256-019-0088-2. S2CID 201827642.
  17. ^ Floridi L, Cowls J (2 July 2019). "A Unified Framework of Five Principles for AI in Society". Harvard Data Science Review. 1. doi:10.1162/99608f92.8cd550d1. S2CID 198775713.
  18. ^ Gabriel I (2018-03-14). "The case for fairer algorithms – Iason Gabriel". Medium. Archived from the original on 2019-07-22. Retrieved 2019-07-22.
  19. ^ "5 unexpected sources of bias in artificial intelligence". TechCrunch. 10 December 2016. Archived from the original on 2021-03-18. Retrieved 2019-07-22.
  20. ^ Knight W. "Google's AI chief says forget Elon Musk's killer robots, and worry about bias in AI systems instead". MIT Technology Review. Archived from the original on 2019-07-04. Retrieved 2019-07-22.
  21. ^ Villasenor J (2019-01-03). "Artificial intelligence and bias: Four key challenges". Brookings. Archived from the original on 2019-07-22. Retrieved 2019-07-22.
  22. ^ Lohr S (9 February 2018). "Facial Recognition Is Accurate, if You're a White Guy". The New York Times. Archived from the original on 9 January 2019. Retrieved 29 May 2019.
  23. ^ Koenecke A, Nam A, Lake E, Nudell J, Quartey M, Mengesha Z, Toups C, Rickford JR, Jurafsky D, Goel S (7 April 2020). "Racial disparities in automated speech recognition". Proceedings of the National Academy of Sciences. 117 (14): 7684–7689. Bibcode:2020PNAS..117.7684K. doi:10.1073/pnas.1915768117. PMC 7149386. PMID 32205437.
  24. ^ Ntoutsi E, Fafalios P, Gadiraju U, Iosifidis V, Nejdl W, Vidal ME, Ruggieri S, Turini F, Papadopoulos S, Krasanakis E, Kompatsiaris I, Kinder-Kurlanda K, Wagner C, Karimi F, Fernandez M (May 2020). "Bias in data-driven artificial intelligence systems—An introductory survey". WIREs Data Mining and Knowledge Discovery. 10 (3). doi:10.1002/widm.1356. ISSN 1942-4787.
  25. ^ "Amazon scraps secret AI recruiting tool that showed bias against women". Reuters. 2018-10-10. Archived from the original on 2019-05-27. Retrieved 2019-05-29.
  26. ^ Friedman B, Nissenbaum H (July 1996). "Bias in computer systems". ACM Transactions on Information Systems. 14 (3): 330–347. doi:10.1145/230538.230561. S2CID 207195759.
  27. ^ "Eliminating bias in AI". Archived from the original on 2019-07-25. Retrieved 2019-07-26.
  28. ^ Abdalla M, Wahle JP, Ruas T, Névéol A, Ducel F, Mohammad S, Fort K (2023). Rogers A, Boyd-Graber J, Okazaki N (eds.). "The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research". Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Toronto, Canada: Association for Computational Linguistics: 13141–13160. arXiv:2305.02797. doi:10.18653/v1/2023.acl-long.734.
  29. ^ Olson P. "Google's DeepMind Has An Idea For Stopping Biased AI". Forbes. Archived from the original on 2019-07-26. Retrieved 2019-07-26.
  30. ^ "Machine Learning Fairness | ML Fairness". Google Developers. Archived from the original on 2019-08-10. Retrieved 2019-07-26.
  31. ^ "AI and bias – IBM Research – US". Archived from the original on 2019-07-17. Retrieved 2019-07-26.
  32. ^ Bender EM, Friedman B (December 2018). "Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science". Transactions of the Association for Computational Linguistics. 6: 587–604. doi:10.1162/tacl_a_00041.
  33. ^ Gebru T, Morgenstern J, Vecchione B, Vaughan JW, Wallach H, Daumé III H, Crawford K (2018). "Datasheets for Datasets". arXiv:1803.09010 [cs.DB].
  34. ^ Pery A (2021-10-06). "Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities". DeepAI. Archived from the original on 2022-02-18. Retrieved 2022-02-18.
  35. ^ Knight W. "Google's AI chief says forget Elon Musk's killer robots, and worry about bias in AI systems instead". MIT Technology Review. Archived from the original on 2019-07-04. Retrieved 2019-07-26.
  36. ^ "Where in the World is AI? Responsible & Unethical AI Examples". Archived from the original on 2020-10-31. Retrieved 2020-10-28.
  37. ^ Ruggieri S, Alvarez JM, Pugnana A, State L, Turini F (2023-06-26). "Can We Trust Fair-AI?". Proceedings of the AAAI Conference on Artificial Intelligence. 37 (13). Association for the Advancement of Artificial Intelligence (AAAI): 15421–15430. doi:10.1609/aaai.v37i13.26798. hdl:11384/136444. ISSN 2374-3468. S2CID 259678387.
  38. ^ Buyl M, De Bie T (2022). "Inherent Limitations of AI Fairness". Communications of the ACM. 67 (2): 48–55. arXiv:2212.06495. doi:10.1145/3624700. hdl:1854/LU-01GMNH04RGNVWJ730BJJXGCY99.
  39. ^ Castelnovo A, Inverardi N, Nanino G, Penco IG, Regoli D (2023). "Fair Enough? A map of the current limitations of the requirements to have "fair" algorithms". arXiv:2311.12435 [cs.AI].
  40. ^ Aizenberg E, Dennis MJ, van den Hoven J (2023-10-21). "Examining the assumptions of AI hiring assessments and their impact on job seekers' autonomy over self-representation". AI & Society. doi:10.1007/s00146-023-01783-1. ISSN 0951-5666.
  41. ^ Federspiel F, Mitchell R, Asokan A, Umana C, McCoy D (May 2023). "Threats by artificial intelligence to human health and human existence". BMJ Global Health. 8 (5): e010435. doi:10.1136/bmjgh-2022-010435. ISSN 2059-7908. PMC 10186390. PMID 37160371.
  42. ^ a b Spindler G (2023), "Different approaches for liability of Artificial Intelligence – Pros and Cons", Liability for AI, Nomos Verlagsgesellschaft mbH & Co. KG, pp. 41–96, doi:10.5771/9783748942030-41, ISBN 978-3-7489-4203-0, retrieved 2023-12-14
  43. ^ Manyika J (2022). "Getting AI Right: Introductory Notes on AI & Society". Daedalus. 151 (2): 5–27. doi:10.1162/daed_e_01897. ISSN 0011-5266.
  44. ^ Imran A, Posokhova I, Qureshi HN, Masood U, Riaz MS, Ali K, John CN, Hussain MI, Nabeel M (2020-01-01). "AI4COVID-19: AI enabled preliminary diagnosis for COVID-19 from cough samples via an app". Informatics in Medicine Unlocked. 20: 100378. doi:10.1016/j.imu.2020.100378. ISSN 2352-9148. PMC 7318970. PMID 32839734.
  45. ^ Cirillo D, Catuara-Solarz S, Morey C, Guney E, Subirats L, Mellino S, Gigante A, Valencia A, Rementeria MJ, Chadha AS, Mavridis N (2020-06-01). "Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare". npj Digital Medicine. 3 (1): 81. doi:10.1038/s41746-020-0288-5. ISSN 2398-6352. PMC 7264169. PMID 32529043.
  46. ^ Ntoutsi E, Fafalios P, Gadiraju U, Iosifidis V, Nejdl W, Vidal ME, Ruggieri S, Turini F, Papadopoulos S, Krasanakis E, Kompatsiaris I, Kinder-Kurlanda K, Wagner C, Karimi F, Fernandez M (May 2020). "Bias in data-driven artificial intelligence systems—An introductory survey". WIREs Data Mining and Knowledge Discovery. 10 (3). doi:10.1002/widm.1356. ISSN 1942-4787.
  47. ^ a b Luo Q, Puett MJ, Smith MD (2023-03-28). "A Perspectival Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, Wikipedia, and YouTube". arXiv:2303.16281v2 [cs.CY].
  48. ^ Busker T, Choenni S, Shoae Bargh M (2023-11-20). "Stereotypes in ChatGPT: An empirical study". Proceedings of the 16th International Conference on Theory and Practice of Electronic Governance. ICEGOV '23. New York, NY, USA: Association for Computing Machinery. pp. 24–32. doi:10.1145/3614321.3614325. ISBN 979-8-4007-0742-1.
  49. ^ Kotek H, Dockum R, Sun D (2023-11-05). "Gender bias and stereotypes in Large Language Models". Proceedings of the ACM Collective Intelligence Conference. CI '23. New York, NY, USA: Association for Computing Machinery. pp. 12–24. arXiv:2308.14921. doi:10.1145/3582269.3615599. ISBN 979-8-4007-0113-9.
  50. ^ Federspiel F, Mitchell R, Asokan A, Umana C, McCoy D (May 2023). "Threats by artificial intelligence to human health and human existence". BMJ Global Health. 8 (5): e010435. doi:10.1136/bmjgh-2022-010435. ISSN 2059-7908. PMC 10186390. PMID 37160371.
  51. ^ Feng S, Park CY, Liu Y, Tsvetkov Y (July 2023). Rogers A, Boyd-Graber J, Okazaki N (eds.). "From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models". Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Toronto, Canada: Association for Computational Linguistics: 11737–11762. arXiv:2305.08283. doi:10.18653/v1/2023.acl-long.656.
  52. ^ Zhou K, Tan C (December 2023). Bouamor H, Pino J, Bali K (eds.). "Entity-Based Evaluation of Political Bias in Automatic Summarization". Findings of the Association for Computational Linguistics: EMNLP 2023. Singapore: Association for Computational Linguistics: 10374–10386. arXiv:2305.02321. doi:10.18653/v1/2023.findings-emnlp.696.
  53. ^ Cheng M, Durmus E, Jurafsky D (2023-05-29). "Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models". arXiv:2305.18189v1 [cs.CL].
  54. ^ Open Source AI. Archived 2016-03-04 at the Wayback Machine Bill Hibbard. 2008 proceedings of the First Conference on Artificial General Intelligence, eds. Pei Wang, Ben Goertzel, and Stan Franklin.
  55. ^ Stewart A, Melton M. "Hugging Face CEO says he's focused on building a 'sustainable model' for the $4.5 billion open-source-AI startup". Business Insider. Retrieved 2024-04-07.
  56. ^ "The open-source AI boom is built on Big Tech's handouts. How long will it last?". MIT Technology Review. Retrieved 2024-04-07.
  57. ^ Yao D (February 21, 2024). "Google Unveils Open Source Models to Rival Meta, Mistral". AI Business.
  58. ^ 7001-2021 - IEEE Standard for Transparency of Autonomous Systems. IEEE. 4 March 2022. pp. 1–54. doi:10.1109/IEEESTD.2022.9726144. ISBN 978-1-5044-8311-7. S2CID 252589405. Retrieved 9 July 2023..
  59. ^ Kamila MK, Jasrotia SS (2023-01-01). "Ethical issues in the development of artificial intelligence: recognizing the risks". International Journal of Ethics and Systems. doi:10.1108/IJOES-05-2023-0107. ISSN 2514-9369. S2CID 259614124.
  60. ^ Thurm S (July 13, 2018). "Microsoft Calls For Federal Regulation of Facial Recognition". Wired. Archived from the original on May 9, 2019. Retrieved January 10, 2019.
  61. ^ Piper K (2024-02-02). "Should we make our most powerful AI models open source to all?". Vox. Retrieved 2024-04-07.
  62. ^ Vincent J (2023-03-15). "OpenAI co-founder on company's past approach to openly sharing research: "We were wrong"". The Verge. Retrieved 2024-04-07.
  63. ^ Inside The Mind Of A.I. Archived 2021-08-10 at the Wayback Machine - Cliff Kuang interview
  64. ^ Bunn J (2020-04-13). "Working in contexts for which transparency is important: A recordkeeping view of explainable artificial intelligence (XAI)". Records Management Journal. 30 (2): 143–153. doi:10.1108/RMJ-08-2019-0038. ISSN 0956-5698. S2CID 219079717.
  65. ^ Li F, Ruijs N, Lu Y (2022-12-31). "Ethics & AI: A Systematic Review on Ethical Concerns and Related Strategies for Designing with AI in Healthcare". AI. 4 (1): 28–53. doi:10.3390/ai4010003. ISSN 2673-2688.
  66. ^ Howard A (29 July 2019). "The Regulation of AI – Should Organizations Be Worried? | Ayanna Howard". MIT Sloan Management Review. Archived from the original on 2019-08-14. Retrieved 2019-08-14.
  67. ^ "Trust in artificial intelligence - A five country study" (PDF). KPMG. March 2021.
  68. ^ Bastin R, Wantz G (June 2017). "The General Data Protection Regulation Cross-industry innovation" (PDF). Inside magazine. Deloitte. Archived (PDF) from the original on 2019-01-10. Retrieved 2019-01-10.
  69. ^ "UN artificial intelligence summit aims to tackle poverty, humanity's 'grand challenges'". UN News. 2017-06-07. Archived from the original on 2019-07-26. Retrieved 2019-07-26.
  70. ^ "Artificial intelligence – Organisation for Economic Co-operation and Development". Archived from the original on 2019-07-22. Retrieved 2019-07-26.
  71. ^ Anonymous (2018-06-14). "The European AI Alliance". Digital Single Market – European Commission. Archived from the original on 2019-08-01. Retrieved 2019-07-26.
  72. ^ European Commission High-Level Expert Group on AI (2019-06-26). "Policy and investment recommendations for trustworthy Artificial Intelligence". Shaping Europe’s digital future – European Commission. Archived from the original on 2020-02-26. Retrieved 2020-03-16.
  73. ^ Fukuda-Parr S, Gibbons E (July 2021). "Emerging Consensus on 'Ethical AI': Human Rights Critique of Stakeholder Guidelines". Global Policy. 12 (S6): 32–44. doi:10.1111/1758-5899.12965. ISSN 1758-5880.
  74. ^ "EU Tech Policy Brief: July 2019 Recap". Center for Democracy & Technology. 2 August 2019. Archived from the original on 2019-08-09. Retrieved 2019-08-09.
  75. ^ Curtis C, Gillespie N, Lockey S (2022-05-24). "AI-deploying organizations are key to addressing 'perfect storm' of AI risks". AI and Ethics. 3 (1): 145–153. doi:10.1007/s43681-022-00163-7. ISSN 2730-5961. PMC 9127285. PMID 35634256. Archived from the original on 2023-03-15. Retrieved 2022-05-29.
  76. ^ a b "Why the world needs a Bill of Rights on AI". Financial Times. 2021-10-18. Retrieved 2023-03-19.
  77. ^ Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K (March 2019). "Artificial intelligence, bias and clinical safety". BMJ Quality & Safety. 28 (3): 231–237. doi:10.1136/bmjqs-2018-008370. ISSN 2044-5415. PMC 6560460. PMID 30636200.
  78. ^ Evans W (2015). "Posthuman Rights: Dimensions of Transhuman Worlds". Teknokultura. 12 (2). doi:10.5209/rev_TK.2015.v12.n2.49072.
  79. ^ Sheliazhenko Y (2017). "Artificial Personal Autonomy and Concept of Robot Rights". European Journal of Law and Political Sciences: 17–21. doi:10.20534/EJLPS-17-1-17-21. Archived from the original on 14 July 2018. Retrieved 10 May 2017.
  80. ^ The American Heritage Dictionary of the English Language, Fourth Edition
  81. ^ Doomen J (2023). "The artificial intelligence entity as a legal person". Information & Communications Technology Law. 32 (3): 277–278. doi:10.1080/13600834.2023.2196827. hdl:1820/c29a3daa-9e36-4640-85d3-d0ffdd18a62c.
  82. ^ "Robots could demand legal rights". BBC News. December 21, 2006. Archived from the original on October 15, 2019. Retrieved January 3, 2010.
  83. ^ Henderson M (April 24, 2007). "Human rights for robots? We're getting carried away". The Times Online. The Times of London. Archived from the original on May 17, 2008. Retrieved May 2, 2010.
  84. ^ "Saudi Arabia bestows citizenship on a robot named Sophia". 26 October 2017. Archived from the original on 2017-10-27. Retrieved 2017-10-27.
  85. ^ Vincent J (30 October 2017). "Pretending to give a robot citizenship helps no one". The Verge. Archived from the original on 3 August 2019. Retrieved 10 January 2019.
  86. ^ Wilks, Yorick, ed. (2010). Close engagements with artificial companions: key social, psychological, ethical and design issues. Amsterdam: John Benjamins Pub. Co. ISBN 978-90-272-4994-4. OCLC 642206106.
  87. ^ Floudas D (2024). "Advanced AI should be treated similar to Weapons of Mass Destruction". DaniWeb. Archived from the original on 2 June 2024. Retrieved 2024-06-02. I anticipate that [the AI rights] controversy, rather than corporate greed or human deviousness, might be the toughest obstacle for an international legal control framework.
  88. ^ Macrae C (September 2022). "Learning from the Failure of Autonomous and Intelligent Systems: Accidents, Safety, and Sociotechnical Sources of Risk". Risk Analysis. 42 (9): 1999–2025. Bibcode:2022RiskA..42.1999M. doi:10.1111/risa.13850. ISSN 0272-4332. PMID 34814229.
  89. ^ Agarwal A, Edelman S (2020). "Functionally effective conscious AI without suffering". Journal of Artificial Intelligence and Consciousness. 7: 39–50. arXiv:2002.05652. doi:10.1142/S2705078520300030. S2CID 211096533.
  90. ^ Thomas Metzinger (February 2021). "Artificial Suffering: An Argument for a Global Moratorim on Synthetic Phenomenology". Journal of Artificial Intelligence and Consciousness. 8: 43–66. doi:10.1142/S270507852150003X. S2CID 233176465.
  91. ^ Chalmers D (March 2023). "Could a Large Language Model be Conscious?". arXiv:2303.07103v1 [Science Computer Science].
  92. ^ a b
  93. ^ a b Joseph Weizenbaum, quoted in McCorduck 2004, pp. 356, 374–376
  94. ^ Kaplan A, Haenlein M (January 2019). "Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence". Business Horizons. 62 (1): 15–25. doi:10.1016/j.bushor.2018.08.004. S2CID 158433736.
  95. ^ a b Hibbard B (17 November 2015). "Ethical Artificial Intelligence". arXiv:1411.1373 [cs.AI].
  96. ^ Davies A (29 February 2016). "Google's Self-Driving Car Caused Its First Crash". Wired. Archived from the original on 7 July 2019. Retrieved 26 July 2019.
  97. ^ Levin S, Wong JC (19 March 2018). "Self-driving Uber kills Arizona woman in first fatal crash involving pedestrian". The Guardian. Archived from the original on 26 July 2019. Retrieved 26 July 2019.
  98. ^ "Who is responsible when a self-driving car has an accident?". Futurism. 30 January 2018. Archived from the original on 2019-07-26. Retrieved 2019-07-26.
  99. ^ "Autonomous Car Crashes: Who – or What – Is to Blame?". Knowledge@Wharton. Law and Public Policy. Radio Business North America Podcasts. Archived from the original on 2019-07-26. Retrieved 2019-07-26.
  100. ^ Delbridge E. "Driverless Cars Gone Wild". The Balance. Archived from the original on 2019-05-29. Retrieved 2019-05-29.
  101. ^ Stilgoe J (2020), "Who Killed Elaine Herzberg?", Who’s Driving Innovation?, Cham: Springer International Publishing, pp. 1–6, doi:10.1007/978-3-030-32320-2_1, ISBN 978-3-030-32319-6, S2CID 214359377, archived from the original on 2021-03-18, retrieved 2020-11-11
  102. ^ Maxmen A (October 2018). "Self-driving car dilemmas reveal that moral choices are not universal". Nature. 562 (7728): 469–470. Bibcode:2018Natur.562..469M. doi:10.1038/d41586-018-07135-0. PMID 30356197.
  103. ^ "Regulations for driverless cars". GOV.UK. Archived from the original on 2019-07-26. Retrieved 2019-07-26.
  104. ^ "Automated Driving: Legislative and Regulatory Action – CyberWiki". Archived from the original on 2019-07-26. Retrieved 2019-07-26.
  105. ^ "Autonomous Vehicles | Self-Driving Vehicles Enacted Legislation". Archived from the original on 2019-07-26. Retrieved 2019-07-26.
  106. ^ Etzioni A, Etzioni O (2017-12-01). "Incorporating Ethics into Artificial Intelligence". The Journal of Ethics. 21 (4): 403–418. doi:10.1007/s10892-017-9252-2. ISSN 1572-8609. S2CID 254644745.
  107. ^ Call for debate on killer robots Archived 2009-08-07 at the Wayback Machine, By Jason Palmer, Science and technology reporter, BBC News, 8/3/09.
  108. ^ Science New Navy-funded Report Warns of War Robots Going "Terminator" Archived 2009-07-28 at the Wayback Machine, by Jason Mick (Blog),, February 17, 2009.
  109. ^ a b Navy report warns of robot uprising, suggests a strong moral compass Archived 2011-06-04 at the Wayback Machine, by Joseph L. Flatley, Feb 18th 2009.
  110. ^ AAAI Presidential Panel on Long-Term AI Futures 2008–2009 Study Archived 2009-08-28 at the Wayback Machine, Association for the Advancement of Artificial Intelligence, Accessed 7/26/09.
  111. ^ United States. Defense Innovation Board. AI principles: recommendations on the ethical use of artificial intelligence by the Department of Defense. OCLC 1126650738.
  112. ^ New Navy-funded Report Warns of War Robots Going "Terminator" Archived 2009-07-28 at the Wayback Machine, by Jason Mick (Blog),, February 17, 2009.
  113. ^ Umbrello S, Torres P, De Bellis AF (March 2020). "The future of war: could lethal autonomous weapons make conflict more ethical?". AI & Society. 35 (1): 273–282. doi:10.1007/s00146-019-00879-x. hdl:2318/1699364. ISSN 0951-5666. S2CID 59606353. Archived from the original on 2021-01-05. Retrieved 2020-11-11.
  114. ^ Hellström T (June 2013). "On the moral responsibility of military robots". Ethics and Information Technology. 15 (2): 99–107. doi:10.1007/s10676-012-9301-2. S2CID 15205810. ProQuest 1372020233.
  115. ^ Mitra A (5 April 2018). "We can train AI to identify good and evil, and then use it to teach us morality". Quartz. Archived from the original on 2019-07-26. Retrieved 2019-07-26.
  116. ^ Dominguez G (23 August 2022). "South Korea developing new stealthy drones to support combat aircraft". The Japan Times. Retrieved 14 June 2023.
  117. ^ "AI Principles". Future of Life Institute. 11 August 2017. Archived from the original on 2017-12-11. Retrieved 2019-07-26.
  118. ^ a b Zach Musgrave and Bryan W. Roberts (2015-08-14). "Why Artificial Intelligence Can Too Easily Be Weaponized – The Atlantic". The Atlantic. Archived from the original on 2017-04-11. Retrieved 2017-03-06.
  119. ^ Cat Zakrzewski (2015-07-27). "Musk, Hawking Warn of Artificial Intelligence Weapons". WSJ. Archived from the original on 2015-07-28. Retrieved 2017-08-04.
  120. ^ "Potential Risks from Advanced Artificial Intelligence". Open Philanthropy. August 11, 2015. Retrieved 2024-04-07.
  121. ^ Brandon Vigliarolo. "International military AI summit ends with 60-state pledge". Retrieved 2023-02-17.
  122. ^ a b Markoff J (25 July 2009). "Scientists Worry Machines May Outsmart Man". The New York Times. Archived from the original on 25 February 2017. Retrieved 24 February 2017.
  123. ^ Muehlhauser, Luke, and Louie Helm. 2012. "Intelligence Explosion and Machine Ethics" Archived 2015-05-07 at the Wayback Machine. In Singularity Hypotheses: A Scientific and Philosophical Assessment, edited by Amnon Eden, Johnny Søraker, James H. Moor, and Eric Steinhart. Berlin: Springer.
  124. ^ Bostrom, Nick. 2003. "Ethical Issues in Advanced Artificial Intelligence" Archived 2018-10-08 at the Wayback Machine. 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.
  125. ^ Bostrom N (2017). Superintelligence: paths, dangers, strategies. Oxford, United Kingdom: Oxford University Press. ISBN 978-0-19-967811-2.
  126. ^ Umbrello S, Baum SD (2018-06-01). "Evaluating future nanotechnology: The net societal impacts of atomically precise manufacturing". Futures. 100: 63–73. doi:10.1016/j.futures.2018.04.007. hdl:2318/1685533. ISSN 0016-3287. S2CID 158503813. Archived from the original on 2019-05-09. Retrieved 2020-11-29.
  127. ^ Yudkowsky, Eliezer. 2011. "Complex Value Systems in Friendly AI" Archived 2015-09-29 at the Wayback Machine. In Schmidhuber, Thórisson, and Looks 2011, 388–393.
  128. ^ Russell S (October 8, 2019). Human Compatible: Artificial Intelligence and the Problem of Control. United States: Viking. ISBN 978-0-525-55861-3. OCLC 1083694322.
  129. ^ Yampolskiy RV (2020-03-01). "Unpredictability of AI: On the Impossibility of Accurately Predicting All Actions of a Smarter Agent". Journal of Artificial Intelligence and Consciousness. 07 (1): 109–118. doi:10.1142/S2705078520500034. ISSN 2705-0785. S2CID 218916769. Archived from the original on 2021-03-18. Retrieved 2020-11-29.
  130. ^ Wallach W, Vallor S (2020-09-17), "Moral Machines: From Value Alignment to Embodied Virtue", Ethics of Artificial Intelligence, Oxford University Press, pp. 383–412, doi:10.1093/oso/9780190905033.003.0014, ISBN 978-0-19-090503-3, archived from the original on 2020-12-08, retrieved 2020-11-29
  131. ^ Umbrello S (2019). "Beneficial Artificial Intelligence Coordination by Means of a Value Sensitive Design Approach". Big Data and Cognitive Computing. 3 (1): 5. doi:10.3390/bdcc3010005. hdl:2318/1685727.
  132. ^ Floridi L, Cowls J, King TC, Taddeo M (2020). "How to Design AI for Social Good: Seven Essential Factors". Science and Engineering Ethics. 26 (3): 1771–1796. doi:10.1007/s11948-020-00213-5. ISSN 1353-3452. PMC 7286860. PMID 32246245.
  133. ^ Fiegerman S (28 September 2016). "Facebook, Google, Amazon create group to ease AI concerns". CNNMoney. Archived from the original on 17 September 2020. Retrieved 18 August 2020.
  134. ^ Slota SC, Fleischmann KR, Greenberg S, Verma N, Cummings B, Li L, Shenefiel C (2023). "Locating the work of artificial intelligence ethics". Journal of the Association for Information Science and Technology. 74 (3): 311–322. doi:10.1002/asi.24638. ISSN 2330-1635. S2CID 247342066.
  135. ^ "Ethics guidelines for trustworthy AI". Shaping Europe’s digital future – European Commission. European Commission. 2019-04-08. Archived from the original on 2020-02-20. Retrieved 2020-02-20.
  136. ^ "White Paper on Artificial Intelligence – a European approach to excellence and trust | Shaping Europe's digital future". 19 February 2020. Archived from the original on 2021-03-06. Retrieved 2021-03-18.
  137. ^ "OECD AI Policy Observatory". Archived from the original on 2021-03-08. Retrieved 2021-03-18.
  138. ^ Recommendation on the Ethics of Artificial Intelligence. UNESCO. 2021.
  139. ^ "UNESCO member states adopt first global agreement on AI ethics". Helsinki Times. 2021-11-26. Retrieved 2023-04-26.
  140. ^ "The Obama Administration's Roadmap for AI Policy". Harvard Business Review. 2016-12-21. ISSN 0017-8012. Archived from the original on 2021-01-22. Retrieved 2021-03-16.
  141. ^ "Accelerating America's Leadership in Artificial Intelligence – The White House". Archived from the original on 2021-02-25. Retrieved 2021-03-16.
  142. ^ "Request for Comments on a Draft Memorandum to the Heads of Executive Departments and Agencies, "Guidance for Regulation of Artificial Intelligence Applications"". Federal Register. 2020-01-13. Archived from the original on 2020-11-25. Retrieved 2020-11-28.
  143. ^ "CCC Offers Draft 20-Year AI Roadmap; Seeks Comments". HPCwire. 2019-05-14. Archived from the original on 2021-03-18. Retrieved 2019-07-22.
  144. ^ "Request Comments on Draft: A 20-Year Community Roadmap for AI Research in the US » CCC Blog". 13 May 2019. Archived from the original on 2019-05-14. Retrieved 2019-07-22.
  145. ^ "Non-Human Party". 2021. Archived from the original on 2021-09-20. Retrieved 2021-09-19.
  146. ^ (in Russian) Интеллектуальные правила Archived 2021-12-30 at the Wayback MachineKommersant, 25.11.2021
  147. ^ Grace K, Salvatier J, Dafoe A, Zhang B, Evans O (2018-05-03). "When Will AI Exceed Human Performance? Evidence from AI Experts". arXiv:1705.08807 [cs.AI].
  148. ^ "China wants to shape the global future of artificial intelligence". MIT Technology Review. Archived from the original on 2020-11-20. Retrieved 2020-11-29.
  149. ^ Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, Luetge C, Madelin R, Pagallo U, Rossi F, Schafer B (2018-12-01). "AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations". Minds and Machines. 28 (4): 689–707. doi:10.1007/s11023-018-9482-5. ISSN 1572-8641. PMC 6404626. PMID 30930541.
  150. ^ "Joanna J. Bryson". WIRED. Retrieved 13 January 2023.
  151. ^ "New Artificial Intelligence Research Institute Launches". 2017-11-20. Archived from the original on 2020-09-18. Retrieved 2021-02-21.
  152. ^ James J. Hughes, LaGrandeur, Kevin, eds. (15 March 2017). Surviving the machine age: intelligent technology and the transformation of human work. Cham, Switzerland: Palgrave Macmillan Cham. ISBN 978-3-319-51165-8. OCLC 976407024. Archived from the original on 18 March 2021. Retrieved 29 November 2020.
  153. ^ Danaher, John (2019). Automation and utopia: human flourishing in a world without work. Cambridge, Massachusetts: Harvard University Press. ISBN 978-0-674-24220-3. OCLC 1114334813.
  154. ^ "TUM Institute for Ethics in Artificial Intelligence officially opened". Archived from the original on 2020-12-10. Retrieved 2020-11-29.
  155. ^ Communications PK (2019-01-25). "Harvard works to embed ethics in computer science curriculum". Harvard Gazette. Retrieved 2023-04-06.
  156. ^ "AI Principles". Future of Life Institute. Retrieved 2023-04-06.
  157. ^ Lee J (2020-02-08). "When Bias Is Coded Into Our Technology". NPR. Archived from the original on 2022-03-26. Retrieved 2021-12-22.
  158. ^ a b "How one conference embraced diversity". Nature. 564 (7735): 161–162. 2018-12-12. doi:10.1038/d41586-018-07718-x. PMID 31123357. S2CID 54481549.
  159. ^ Roose K (2020-12-30). "The 2020 Good Tech Awards". The New York Times. ISSN 0362-4331. Archived from the original on 2021-12-21. Retrieved 2021-12-21.
  160. ^ Lodge P (2014). "Leibniz's Mill Argument Against Mechanical Materialism Revisited". Ergo, an Open Access Journal of Philosophy. 1 (20201214). doi:10.3998/ergo.12405314.0001.003. hdl:2027/spo.12405314.0001.003. ISSN 2330-4014.
  161. ^ Bringsjord S, Govindarajulu NS (2020), "Artificial Intelligence", in Zalta EN, Nodelman U (eds.), The Stanford Encyclopedia of Philosophy (Summer 2020 ed.), Metaphysics Research Lab, Stanford University, retrieved 2023-12-08
  162. ^ Jr HC (1999-04-29). Information Technology and the Productivity Paradox: Assessing the Value of Investing in IT. Oxford University Press. ISBN 978-0-19-802838-3.
  163. ^ Asimov I (2008). I, Robot. New York: Bantam. ISBN 978-0-553-38256-3.
  164. ^ Bryson J, Diamantis M, Grant T (September 2017). "Of, for, and by the people: the legal lacuna of synthetic persons". Artificial Intelligence and Law. 25 (3): 273–291. doi:10.1007/s10506-017-9214-9.
  165. ^ "Principles of robotics". UK's EPSRC. September 2010. Archived from the original on 1 April 2018. Retrieved 10 January 2019.
  166. ^ Yudkowsky E (July 2004). "Why We Need Friendly AI". 3 laws unsafe. Archived from the original on May 24, 2012.
  167. ^ Aleksander I (March 2017). "Partners of Humans: A Realistic Assessment of the Role of Robots in the Foreseeable Future". Journal of Information Technology. 32 (1): 1–9. doi:10.1057/s41265-016-0032-4. ISSN 0268-3962. S2CID 5288506.
  168. ^ Evolving Robots Learn To Lie To Each Other Archived 2009-08-28 at the Wayback Machine, Popular Science, August 18, 2009
  169. ^ Bassett C, Steinmueller E, Voss G. "Better Made Up: The Mutual Influence of Science Fiction and Innovation". Nesta. Retrieved 3 May 2024.
  170. ^ Velasco G (2020-05-04). "Science-Fiction: A Mirror for the Future of Humankind". IDEES. Retrieved 2023-12-08.
  171. ^ Hodges, A. (2014), Alan Turing: The Enigma, Vintage, London, p. 334
  172. ^ A. M. Turing (1936). "On computable numbers, with an application to the Entscheidungsproblem." in Proceedings of the London Mathematical Society, 2 s. vol. 42 (1936–1937), pp. 230–265.
  173. ^ "Love, Death & Robots season 2, episode 1 recap - "Automated Customer Service"". Ready Steady Cut. 2021-05-14. Archived from the original on 2021-12-21. Retrieved 2021-12-21.
  174. ^ Cave, Stephen, Dihal, Kanta, Dillon, Sarah, eds. (14 February 2020). AI narratives: a history of imaginative thinking about intelligent machines (First ed.). Oxford: Oxford University Press. ISBN 978-0-19-258604-9. OCLC 1143647559. Archived from the original on 18 March 2021. Retrieved 11 November 2020.
  175. ^ Jerreat-Poole A (1 February 2020). "Sick, Slow, Cyborg: Crip Futurity in Mass Effect". Game Studies. 20. ISSN 1604-7982. Archived from the original on 9 December 2020. Retrieved 11 November 2020.
  176. ^ ""Detroit: Become Human" Will Challenge your Morals and your Humanity". Coffee or Die Magazine. 2018-08-06. Archived from the original on 2021-12-09. Retrieved 2021-12-07.
  177. ^ Cerqui D, Warwick K (2008), "Re-Designing Humankind: The Rise of Cyborgs, a Desirable Goal?", Philosophy and Design, Dordrecht: Springer Netherlands, pp. 185–195, doi:10.1007/978-1-4020-6591-0_14, ISBN 978-1-4020-6590-3, archived from the original on 2021-03-18, retrieved 2020-11-11
  178. ^ Cave S, Dihal K (6 August 2020). "The Whiteness of AI". Philosophy & Technology. 33 (4): 685–703. doi:10.1007/s13347-020-00415-6. S2CID 225466550.