Computational ethics (bibtex)
	abstract = {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.},
	author = {Edmond Awad and Sydney Levine and Michael Anderson and Susan Leigh Anderson and Vincent Conitzer and M.J. Crockett and Jim A.C. Everett and Theodoros Evgeniou and Alison Gopnik and Julian C. Jamison and Tae Wan Kim and S. Matthew Liao and Michelle N. Meyer and John Mikhail and Kweku Opoku-Agyemang and Jana Schaich Borg and Juliana Schroeder and Walter Sinnott-Armstrong and Marija Slavkovik and Josh B. Tenenbaum},
	date-added = {2022-04-11 18:57:19 +0200},
	date-modified = {2022-04-11 18:57:19 +0200},
	doi = {},
	issn = {1364-6613},
	journal = {Trends in Cognitive Sciences},
	keywords = {ethics, computation, moral psychology, AI ethics, machine ethics, moral cognition},
	title = {Computational ethics},
	url = {},
	year = {2022},
	bdsk-url-1 = {},
	bdsk-url-2 = {}}
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