Machine ethics

Machine ethics (or machine morality, computational morality, or computational ethics) is a part of the ethics of artificial intelligence concerned with adding or ensuring moral behaviors of man-made machines that use artificial intelligence, otherwise known as artificial intelligent agents. Machine ethics differs from other ethical fields related to engineering and technology. It should not be confused with computer ethics, which focuses on human use of computers. It should also be distinguished from the philosophy of technology, which concerns itself with technology's grander social effects.

Definitions
James H. Moor, one of the pioneering theoreticians in the field of computer ethics, defines four kinds of ethical robots. As an extensive researcher on the studies of philosophy of artificial intelligence, philosophy of mind, philosophy of science, and logic, Moor defines machines as ethical impact agents, implicit ethical agents, explicit ethical agents, or full ethical agents. A machine can be more than one type of agent. (See artificial systems and moral responsibility.)
 * Ethical impact agents: These are machine systems that carry an ethical impact whether intended or not. At the same time, they have the potential to act unethically. Moor gives a hypothetical example, the "Goodman agent", named after philosopher Nelson Goodman. The Goodman agent compares dates but has the millennium bug. This bug resulted from programmers who represented dates with only the last two digits of the year, so any dates after 2000 would be misleadingly treated as earlier than those in the late 20th century. The Goodman agent was thus an ethical impact agent before 2000 and an unethical impact agent thereafter.
 * Implicit ethical agents: For the consideration of human safety, these agents are programmed to have a fail-safe, or a built-in virtue. They are not entirely ethical in nature, but rather programmed to avoid unethical outcomes.
 * Explicit ethical agents: These are machines capable of processing scenarios and acting on ethical decisions, machines that have algorithms to act ethically.
 * Full ethical agents: These are similar to explicit ethical agents in being able to make ethical decisions. But they also have human metaphysical features (i.e., have free will, consciousness, and intentionality).

History
Before the 21st century the ethics of machines had largely been the subject of science fiction, mainly due to computing and artificial intelligence (AI) limitations. Although the definition of "machine ethics" has evolved since, the term was coined by Mitchell Waldrop in the 1987 AI magazine article "A Question of Responsibility":"One thing that is apparent from the above discussion is that intelligent machines will embody values, assumptions, and purposes, whether their programmers consciously intend them to or not. Thus, as computers and robots become more and more intelligent, it becomes imperative that we think carefully and explicitly about what those built-in values are. Perhaps what we need is, in fact, a theory and practice of machine ethics, in the spirit of Asimov's three laws of robotics."

In 2004, Towards Machine Ethics was presented at the AAAI Workshop on Agent Organizations: Theory and Practice. Theoretical foundations for machine ethics were laid out.

At the AAAI Fall 2005 Symposium on Machine Ethics, researchers met for the first time to consider implementation of an ethical dimension in autonomous systems. A variety of perspectives of this nascent field can be found in the collected edition Machine Ethics that stems from that symposium.

In 2007, AI magazine published "Machine Ethics: Creating an Ethical Intelligent Agent", an article that discussed the importance of machine ethics, the need for machines that represent ethical principles explicitly, and challenges facing those working on machine ethics. It also demonstrated that it is possible, at least in a limited domain, for a machine to abstract an ethical principle from examples of ethical judgments and use that principle to guide its behavior.

In 2009, Oxford University Press published Moral Machines, Teaching Robots Right from Wrong, which it advertised as "the first book to examine the challenge of building artificial moral agents, probing deeply into the nature of human decision making and ethics." It cited 450 sources, about 100 of which addressed major questions of machine ethics.

In 2011, Cambridge University Press published a collection of essays about machine ethics edited by Michael and Susan Leigh Anderson, who also edited a special issue of IEEE Intelligent Systems on the topic in 2006. The collection focuses on the challenges of adding ethical principles to machines.

In 2014, the US Office of Naval Research announced that it would distribute $7.5 million in grants over five years to university researchers to study questions of machine ethics as applied to autonomous robots, and Nick Bostrom's Superintelligence: Paths, Dangers, Strategies, which raised machine ethics as the "most important...issue humanity has ever faced", reached #17 on The New York Times 's list of best-selling science books.

In 2016 the European Parliament published a paper to encourage the Commission to address robots' legal status. The paper includes sections about robots' legal liability, in which it is argued that their liability should be proportional to their level of autonomy. The paper also discusses how many jobs could be taken by AI robots.

In 2019 the Proceedings of the IEEE published a special issue on Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems, edited by Alan Winfield, Katina Michael, Jeremy Pitt and Vanessa Evers. "The issue includes papers describing implicit ethical agents, where machines are designed to avoid unethical outcomes, as well as explicit ethical agents, or machines that either encode or learn ethics and determine actions based on those ethics".

AI control problem
Some scholars, such as Bostrom and AI researcher Stuart Russell, argue that, if AI surpasses humanity in general intelligence and becomes "superintelligent", this new superintelligence could become powerful and difficult to control: just as the mountain gorilla's fate depends on human goodwill, so might humanity's fate depend on a future superintelligence's actions. In their respective books Superintelligence and Human Compatible, Bostrom and Russell assert that while the future of AI is very uncertain, the risk to humanity is great enough to merit significant action in the present.

This presents the AI control problem: how to build an intelligent agent that will aid its creators without inadvertently building a superintelligence that will harm them. The danger of not designing control right "the first time" is that a superintelligence may be able to seize power over its environment and prevent us from shutting it down. Potential AI control strategies include "capability control" (limiting an AI's ability to influence the world) and "motivational control" (one way of building an AI whose goals are aligned with human or optimal values). A number of organizations are researching the AI control problem, including the Future of Humanity Institute, the Machine Intelligence Research Institute, the Center for Human-Compatible Artificial Intelligence, and the Future of Life Institute.

Algorithms and training
AI paradigms have been debated, especially their efficacy and bias. Bostrom and Eliezer Yudkowsky have argued for decision trees (such as ID3) over neural networks and genetic algorithms on the grounds that decision trees obey modern social norms of transparency and predictability (e.g. stare decisis). In contrast, Chris Santos-Lang has argued in favor of neural networks and genetic algorithms 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 than machines to criminal hackers.

In 2009, in an experiment at the Ecole Polytechnique Fédérale of Lausanne's Laboratory of Intelligent Systems, AI robots were programmed to cooperate with each other and tasked with searching for a beneficial resource while avoiding a poisonous one. During the experiment, the robots were grouped into clans, and the successful members' digital genetic code was used for the next generation, a type of algorithm known as a genetic algorithm. After 50 successive generations in the AI, one clan's members discovered how to distinguish the beneficial resource from the poisonous one. The robots then learned to lie to each other in an attempt to hoard the beneficial resource from other robots. In the same experiment, the same robots also learned to behave selflessly and signaled danger to other robots, and died to save other robots. Machine ethicists have questioned the experiment's implications. In the experiment, the robots' goals were programmed to be "terminal", but human motives typically require never-ending learning.

Autonomous weapons systems
In 2009, academics and technical experts attended a conference to discuss the potential impact of robots and computers and the impact of the possibility that they could become self-sufficient and able to make their own decisions. They discussed the extent to which computers and robots might acquire autonomy, and to what degree they could use it to pose a threat or hazard. They noted that some machines have acquired various forms of semi-autonomy, including the ability to find power sources on their own and 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 are other potential hazards and pitfalls.

Some experts and academics have questioned the use of robots in military combat, especially robots with a degree of autonomy. The U.S. Navy funded a report that indicates that as military robots become more complex, we should pay greater attention to the implications of their ability to make autonomous decisions. The president of the Association for the Advancement of Artificial Intelligence has commissioned a study of this issue.

Integration of artificial general intelligences with society
Preliminary work has been conducted on methods of integrating artificial general intelligences (full ethical agents as defined above) with existing legal and social frameworks. Approaches have focused on their legal position and rights.

Machine learning bias
Big data and machine learning algorithms have become popular in numerous industries, including online advertising, credit ratings, and criminal sentencing, with the promise of providing more objective, data-driven results, but have been identified as a potential way to perpetuate social inequalities and discrimination. A 2015 study found that women were less likely than men to be shown high-income job ads by Google's AdSense. Another study found that Amazon's same-day delivery service was intentionally made unavailable in black neighborhoods. Both Google and Amazon were unable to isolate these outcomes to a single issue, and said the outcomes were the result of the black box algorithms they use.

The U.S. judicial system has begun using quantitative risk assessment software when making decisions related to releasing people on bail and sentencing in an effort to be fairer and reduce the imprisonment rate. These tools analyze a defendant's criminal history, among other attributes. In a study of 7,000 people arrested in Broward County, Florida, only 20% of people predicted to commit a crime using the county's risk assessment scoring system proceeded to commit a crime. A 2016 ProPublica report analyzed recidivism risk scores calculated by one of the most commonly used tools, the Northpointe COMPAS system, and looked at outcomes over two years. The report found that only 61% of those deemed high-risk committed additional crimes during that period. The report also flagged that African-American defendants were far more likely to be given high-risk scores than their white counterparts. It has been argued that such pretrial risk assessments violate Equal Protection rights on the basis of race, due to factors including possible discriminatory intent by the algorithm itself, under a theory of partial legal capacity for artificial intelligences.

In 2016, the Obama administration's Big Data Working Group—an overseer of various big-data regulatory frameworks—released reports warning of "the potential of encoding discrimination in automated decisions" and calling for "equal opportunity by design" for applications such as credit scoring. The reports encourage discourse among policy-makers, citizens, and academics alike, but recognize that no solution yet exists for the encoding of bias and discrimination into algorithmic systems.

Practices
In March 2018, in an effort to address rising concerns over machine learning's impact on human rights, the World Economic Forum and Global Future Council on Human Rights published a white paper with detailed recommendations on how best to prevent discriminatory outcomes in machine learning. The World Economic Forum developed four recommendations based on the UN Guiding Principles of Human Rights to help address and prevent discriminatory outcomes in machine learning:


 * 1) Active inclusion: Development and design of machine learning applications must actively seek a diversity of input, especially of the norms and values of populations affected by the output of AI systems.
 * 2) Fairness: People involved in conceptualizing, developing, and implementing machine learning systems should consider which definition of fairness best applies to their context and application, and prioritize it in the machine learning system's architecture and evaluation metrics.
 * 3) Right to understanding: Involvement of machine learning systems in decision-making that affects individual rights must be disclosed, and the systems must be able to explain their decision-making in a way that is understandable to end users and reviewable by a competent human authority. Where this is impossible and rights are at stake, leaders in the design, deployment, and regulation of machine learning technology must question whether it should be used.
 * 4) Access to redress: Leaders, designers, and developers of machine learning systems are responsible for identifying the potential negative human rights impacts of their systems. They must make visible avenues for redress for those affected by disparate impacts, and establish processes for the timely redress of any discriminatory outputs.

In January 2020, Harvard University's Berkman Klein Center for Internet and Society published a meta-study of 36 prominent sets of principles for AI, identifying eight key themes: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values. Researchers at the Swiss Federal Institute of Technology in Zurich conducted a similar meta-study in 2019.

Approaches
There have been several attempts to make ethics computable, or at least formal. Isaac Asimov's Three Laws of Robotics are not usually considered suitable for an artificial moral agent, but whether Kant's categorical imperative can be used has been studied. It has been pointed out that human value is, in some aspects, very complex. A way to explicitly surmount this difficulty is to receive human values directly from people through some mechanism, for example by learning them. Another approach is to base current ethical considerations on previous similar situations. This is called casuistry, and could be implemented through research on the Internet. The consensus from a million past decisions would lead to a new decision that is democracy-dependent. Bruce M. McLaren built an early (mid-1990s) computational model of casuistry, a program called SIROCCO built with AI and case-base reasoning techniques that retrieves and analyzes ethical dilemmas. But this approach could lead to decisions that reflect society's biases and unethical behavior. The negative effects of this approach can be seen in Microsoft's Tay, a chatterbot that learned to repeat racist and sexually charged tweets.

One thought experiment focuses on a Genie Golem with unlimited powers presenting itself to the reader. This Genie declares that it will return in 50 years and demands that it be provided with a definite set of morals it will then immediately act upon. This experiment's purpose is to spark discourse over how best to handle defining sets of ethics that computers may understand.

Some recent work attempts to reconstruct AI morality and control more broadly as a problem of mutual contestation between AI as a Foucauldian subjectivity on the one hand and humans or institutions on the other hand, all within a disciplinary apparatus. Certain desiderata need to be fulfilled: embodied self-care, embodied intentionality, imagination and reflexivity, which together would condition AI's emergence as an ethical subject capable of self-conduct.

In fiction
In science fiction, movies and novels have played with the idea of sentient robots and machines.

Neill Blomkamp's Chappie (2015) enacts a scenario of being able to transfer one's consciousness into a computer. Alex Garland's 2014 film Ex Machina follows an android with artificial intelligence undergoing a variation of the Turing Test, a test administered to a machine to see whether its behavior can be distinguished from that of a human. Films such as The Terminator (1984) and The Matrix (1999) incorporate the concept of machines turning on their human masters.

Asimov considered the issue in the 1950s 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 his three laws' boundaries to see where they break down or create paradoxical or unanticipated behavior. His work suggests that no set of fixed laws can sufficiently anticipate all possible circumstances. Philip K. Dick's 1968 novel Do Androids Dream of Electric Sheep? explores what it means to be human. In his post-apocalyptic scenario, he questions whether empathy is an entirely human characteristic. The book is the basis for the 1982 science-fiction film Blade Runner.

Related fields

 * Affective computing
 * Formal ethics
 * Bioethics
 * Computational theory of mind
 * Computer ethics
 * Ethics of artificial intelligence
 * Moral psychology
 * Philosophy of artificial intelligence
 * Philosophy of mind