Computational ethics

Technological advances are enabling roles for machines that present novel ethical challenges. The study of 'AI ethics' has emerged to confront these challenges, and connects perspectives from philosophy, computer science, law, and economics. Less represented in these interdisciplinary efforts is the perspective of cognitive science. We propose a framework – computational ethics – that specifies how the ethical challenges of AI can be partially addressed by incorporating the study of human moral decision-making. The driver of this framework is a computational version of reflective equilibrium (RE), an approach that seeks coherence between considered judgments and governing principles. The framework has two goals: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms. Working jointly towards these two goals will create the opportunity to integrate diverse research questions, bring together multiple academic communities, uncover new interdisciplinary research topics, and shed light on centuries-old philosophical questions

Computational ethics

Technological advances are enabling roles for machines that present novel ethical challenges. The study of 'AI ethics' has emerged to confront these challenges, and connects perspectives from philosophy, computer science, law, and economics. Less represented in these interdisciplinary efforts is the perspective of cognitive science. We propose a framework – computational ethics – that specifies how the ethical challenges of AI can be partially addressed by incorporating the study of human moral decision-making. The driver of this framework is a computational version of reflective equilibrium (RE), an approach that seeks coherence between considered judgments and governing principles. The framework has two goals: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms. Working jointly towards these two goals will create the opportunity to integrate diverse research questions, bring together multiple academic communities, uncover new interdisciplinary research topics, and shed light on centuries-old philosophical questions.publishedVersio

Automated Influence and the Challenge of Cognitive Security

Advances in AI are powering increasingly precise and widespread computational propaganda, posing serious threats to national security. The military and intelligence communities are starting to discuss ways to engage in this space, but the path forward is still unclear. These developments raise pressing ethical questions, about which existing ethics frameworks are silent. Understanding these challenges through the lens of “cognitive security,” we argue, offers a promising approach

Computational ethics

This is the final version. Available on open access from Elsevier via the DOI in this recordTechnological advances are enabling roles for machines that present novel ethical challenges. The study of 'AI ethics' has emerged to confront these challenges, and connects perspectives from philosophy, computer science, law, and economics. Less represented in these interdisciplinary efforts is the perspective of cognitive science. We propose a framework – computational ethics – that specifies how the ethical challenges of AI can be partially addressed by incorporating the study of human moral decision-making. The driver of this framework is a computational version of reflective equilibrium (RE), an approach that seeks coherence between considered judgments and governing principles. The framework has two goals: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms. Working jointly towards these two goals will create the opportunity to integrate diverse research questions, bring together multiple academic communities, uncover new interdisciplinary research topics, and shed light on centuries-old philosophical questions.Templeton World Charity Foundatio

From machine ethics to computational ethics

Abstract: Research into the ethics of artificial intelligence is often categorized into two subareas – robot ethics and machine ethics. Many of the definitions and classifications of the subject matter of these subfields, as found in the literature, are conflated, which I seek to rectify. In this essay, I infer that using the term ‘machine ethics’ is too broad and glosses over issues that the term computational ethics best describes. I show that the subject of inquiry of computational ethics is of great value and indeed is an important frontier in developing ethical artificial intelligence systems (AIS). I also show that computational is a distinct, often neglected field in the ethics of AI. In contrast to much of the literature, I argue that the appellation ‘machine ethics’ does not sufficiently capture the entire project of embedding ethics into AI/S and hence the need for computational ethics. This essay is unique for two reasons; first, it offers a philosophical analysis of the subject of computational ethics that is not found in the literature. Second, it offers a finely grained analysis that shows the thematic distinction among robot ethics, machine ethics and computational ethics

The Importance of Formative Assessment in Science and Engineering Ethics Education: Some Evidence and Practical Advice

Recent research in ethics education shows a potentially problematic variation in content, curricular materials, and instruction. While ethics instruction is now widespread, studies have identified significant variation in both the goals and methods of ethics education, leaving researchers to conclude that many approaches may be inappropriately paired with goals that are unachievable. This paper speaks to these concerns by demonstrating the importance of aligning classroom-based assessments to clear ethical learning objectives in order to help students and instructors track their progress toward meeting those objectives. Two studies at two different universities demonstrate the usefulness of classroom-based, formative assessments for improving the quality of students’ case responses in computational modeling and research ethics

Teaching AI Ethics: Observations and Challenges

This report summarises the experience in teaching Artificial Intelligence (AI) Ethics as an elective masters level course at the University of Bergen. The goal of the summary is twofold: 1) to draw lessons for teaching this in-high demand very new discipline; 2) to serve as a basis in developing a bachelor level AI Ethics course for students of artificial intelligence. AI Ethics as a topic is particularly challenging to teach as the discipline itself is very new and no textbooks have been established. The added challenge is introducing methodologies and skills from humanity- and social sciences to students of computational and information sciences.publishedVersio

Teaching AI Ethics: Observations and Challenges

This report summarises the experience in teaching Artificial Intelligence (AI) Ethics as an elective masters level course at the University of Bergen. The goal of the summary is twofold: 1) to draw lessons for teaching this in-high demand very new discipline; 2) to serve as a basis in developing a bachelor level AI Ethics course for students of artificial intelligence. AI Ethics as a topic is particularly challenging to teach as the discipline itself is very new and no textbooks have been established. The added challenge is introducing methodologies and skills from humanity- and social sciences to students of computational and information sciences