Machine Ethics and Robot Ethics

Wendell Wallach, Peter Asaro, Wendell Wallach, Peter Asaro

• Publisher: Routledge
• Year: 2020
• Print ISBN: 9781472430397
• eBook ISBN: 9781000151978
• Topic: Philosophy
• Subtopic: Philosophy & Ethics in Science

Once the stuff of science fiction, recent progress in artificial intelligence, robotics, and machine learning means that these rapidly advancing technologies are finally coming into widespread use within everyday life. Such rapid development in these areas also brings with it a host of social, political and legal issues, as well as a rise in public concern and academic interest in the ethical challenges these new technologies pose. This volume is a collection of scholarly work from leading figures in the development of both robot ethics and machine ethics; it includes essays of historical significance which have become foundational for research in these two new areas of study, as well as important recent articles. The research articles selected focus on the control and governance of computational systems; the exploration of ethical and moral theories using software and robots as laboratories or simulations; inquiry into the necessary requirements for moral agency and the basis and boundaries of rights; and questions of how best to design systems that are both useful and morally sound. Collectively the articles ask what the practical ethical and legal issues, arising from the development of robots, will be over the next twenty years and how best to address these future considerations.

Appendix 1

The Future of Life Institute: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter

http://futureoflife.org/misc/open_letter

Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents—systems that perceive and act in some environment. In this context, “intelligence” is related to statistical and economic notions of rationality—colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems.

As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty is not unfathomable. Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls.

The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008–09 Presidential Panel on Long-Term AI Futures and other projects on AI impacts, and constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. The attached research priorities document gives many examples of such research directions that can help maximize the societal benefit of AI. This research is by necessity interdisciplinary, because it involves both society and AI. It ranges from economics, law, and philosophy to computer security, formal methods and, of course, various branches of AI itself.

In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.

Appendix 2

Research priorities for robust and beneficial artificial intelligence

Last updated January 23, 2015*

Executive Summary: Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to research how to maximize these benefits while avoiding potential pitfalls. This document gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneficial.

1 Artificial Intelligence Today

Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents – systems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality – colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods has led to a large degree of integration and cross-fertilization between AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems.

As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable. Because of the great potential of AI, it is valuable to investigate how to reap its benefits while avoiding potential pitfalls.

The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008–09 Presidential Panel on Long-Term AI Futures [43] and other projects and community efforts on AI impacts. These constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. The present document can be viewed as a natural continuation of these efforts, focusing on identifying research directions that can help maximize the societal benefit of AI. This research is by necessity interdisciplinary, because it involves both society and AI. It ranges from economics, law, and philosophy to computer security, formal methods and, of course, various branches of AI itself. The focus is on delivering AI that is beneficial to society and robust in the sense that the benefits are guaranteed: our AI systems must do what we want them to do.

2 Short-term Research Priorities

2.1 Optimizing AI’s Economic Impact

The successes of industrial applications of AI, from manufacturing to information services, demonstrate a growing impact on the economy, although there is disagreement about the exact nature of this impact and on how to distinguish between the effects of AI and those of other information technologies. Many economists and computer scientists agree that there is valuable research to be done on how to maximize the economic benefits of AI while mitigating adverse effects, which could include increased inequality and unemployment [51, 12, 26, 27, 72, 53, 49]. Such considerations motivate a range of research directions, spanning areas from economics to psychology. Below are a few examples that should by no means be interpreted as an exhaustive list.

1. Labor market forecasting: When and in what order should we expect various jobs to become automated [26]? How will this affect the wages of less skilled workers, creatives, and different kinds of information workers? Some have have argued that AI is likely to greatly increase the overall wealth of humanity as a whole [12]. However, increased automation may push income distribution further towards a power law [13], and the resulting disparity may fall disproportionately along lines of race, class, and gender; research anticipating the economic and societal impact of such disparity could be useful.

2. Other market disruptions: Significant parts of the economy, including finance, insurance, actuarial, and many consumer markets, could be susceptible to disruption through the use of AI techniques to learn, model, and predict agent actions. These markets might be indentified by a combination of high complexity and high rewards for navigating that complexity [49].

3. Policy for managing adverse effects: What policies could help increasingly automated societies flourish? For example, Brynjolfsson and McAfee [12] explore various policies for incentivizing development of labor-intensive sectors and for using AI-generated wealth to support underemployed populations. What are the pros and cons of interventions such as educational reform, apprenticeship programs, labor-demanding infrastructure projects, and changes to minimum wage law, tax structure, and the social safety net [27]? History provides many examples of subpopulations not needing to work for economic security, ranging from aristocrats in antiquity to many present-day citizens of Qatar. What societal structures and other factors determine whether such populations flourish? Unemployment is not the same as leisure, and there are deep links between unemployment and unhappiness, self-doubt, and isolation [35, 19]; understanding ...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Acknowledgments
• Series Preface
• Introduction
• Appendix 1: The Future of Life Institute: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter
• Appendix 2: Research Priorities for Robust and Beneficial Artificial Intelligence
• Part I: Laying foundations
• Part II: Robot ethics
• Part III: Machine ethics
• Part IV: Moral agents and agency
• Part V: Law and policy
• Index