Morten Bay (USC Annenberg School For Communication and Journalism) has posted AI Ethics and Policymaking: Rawlsian Approaches to Democratic Participation, Transparency, Accountability, and Prediction on SSRN. Here is the abstract:
The AI ethics field is seeing an increase in explorations of theoretical ethics in addition to applied ethics, and this has spawned a renewed interest in John Rawls’ theory of justice as fairness and how it may apply to AI. But how may these new, Rawlsian contributions inform regulatory policies for AI? This article takes a Rawlsian approach to four key policy criteria in AI regulation: Democratic participation, transparency, accountability, and the epistemological value of prediction. Rawlsian, democratic participation in the light of AI is explored through a critique of Ashrafian’s proposed approach to Rawlsian AI ethics, which is found to contradict other aspects of Rawls’ theories. A turn toward Gabriel’s foundational theoretical work on Rawlsian justice in AI follows, extending his explication of Rawls’ Publicity criterion to an exploration of how the latter can be applied to real-world AI regulation and policy. Finally, a discussion of a key AI feature, prediction, demonstrates how AI-driven, long-term, large-scale predictions of human behavior violate Rawls’ justice as fairness principles. It is argued that applications of this kind are expressions of the type of utilitarianism Rawls vehemently opposes, and therefore cannot be allowed in Rawls-inspired policymaking.