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 (AI), otherwise known as AI 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.

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 (AI), otherwise known as AI 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. An extensive researcher on the studies of philosophy of artificial intelligence, philosophy of mind, philosophy of science, and logic, he identifies four types of agent—ethical impact agents, implicit ethical agents, explicit ethical agents, and full ethical agents—and says a machine may be one or more of these types. 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). (See artificial systems and moral responsibility.) ==Areas of focus== ===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. ==Ethical frameworks and practices== ===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: 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. 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. 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. 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. While there is no evidence that McLaren’s system exhibited biases of any kind, casuistry does, in general, have the potential to make 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. ==See also== ==References== ==Further reading== Hagendorff, Thilo (2021). Linking Human And Machine Behavior: A New Approach to Evaluate Training Data Quality for Beneficial Machine Learning. Minds and Machines, . Anderson, Michael; Anderson, Susan Leigh, eds (July/August 2006). "Special Issue on Machine Ethics". IEEE Intelligent Systems 21 (4): 10–63. Bendel, Oliver (December 11, 2013). Considerations about the Relationship between Animal and Machine Ethics. AI & SOCIETY, . Dabringer, Gerhard, ed. (2010). "Ethical and Legal Aspects of Unmanned Systems. Interviews". Austrian Ministry of Defence and Sports, Vienna 2010, . Gardner, A. (1987). An Artificial Approach to Legal Reasoning. Cambridge, MA: MIT Press. Georges, T. M. (2003). Digital Soul: Intelligent Machines and Human Values. Cambridge, MA: Westview Press. Singer, P.W. (December 29, 2009). Wired for War: The Robotics Revolution and Conflict in the 21st Century: Penguin. Winfield, A., Michael, K., Pitt, J. and Evers, V. (March 2019). Special Issue on Machine Ethics: The Design and Governance of Ethical AI and Autonomous Systems. Proceedings of the IEEE. 107 (3): 501–615, ==External links== Machine Ethics, Interdisciplinary project on machine ethics. The Machine Ethics Podcast, Podcast discussing Machine Ethics, AI and Tech ethics.

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