The dominant theory of the evolution of moral cognition across a variety of fields is that moral cognition is a biological adaptation to foster social cooperation. This chapter argues, to the contrary, that moral cognition is likely an evolutionary exaptation: a form of cognition where neurobiological capacities selected for in our evolutionary history for a variety of different reasons—many unrelated to social cooperation—were put to a new, prosocial use after the fact through individual rationality, learning, and the development and transmission (...) of social norms. This chapter begins with a brief overview of the emerging behavioral neuroscience of moral cognition. It then outlines a novel theory of moral cognition that I have previously argued explains these findings better than alternatives. Finally, it shows how the evidence for this theory of moral cognition and human evolutionary history together suggest that moral cognition is likely not a biological adaptation. Instead, like reading sheet music or riding a bicycle, moral cognition is something that individuals learn to do—in this case, in response to sociocultural norms created in our ancestral history and passed down through the ages to enable cooperative living. (shrink)

This article argues that existing approaches to programming ethical AI fail to resolve a serious moral-semantic trilemma, generating interpretations of ethical requirements that are either too semantically strict, too semantically flexible, or overly unpredictable. This paper then illustrates the trilemma utilizing a recently proposed ‘general ethical dilemma analyzer,’ GenEth. Finally, it uses empirical evidence to argue that human beings resolve the semantic trilemma using general cognitive and motivational processes involving ‘mental time-travel,’ whereby we simulate different possible pasts and futures. I (...) demonstrate how mental time-travel psychology leads us to resolve the semantic trilemma through a six-step process of interpersonal negotiation and renegotiation, and then conclude by showing how comparative advantages in processing power would plausibly cause AI to use similar processes to solve the semantic trilemma more reliably than we do, leading AI to make better moral-semantic choices than humans do by our very own lights. (shrink)

This article argues that diverse theorists have reasons to theorize about fairness in nonideal conditions, including theorists who reject fairness in ideal theory. It then develops a new all-purpose model of ‘nonideal fairness.’ §1 argues that fairness is central to nonideal theory across diverse ideological and methodological frameworks. §2 then argues that ‘nonideal fairness’ is best modeled by a nonideal original position adaptable to different nonideal conditions and background normative frameworks (including anti-Rawlsian ones). §3 then argues that the parties to (...) the model have grounds to seek a variety of remedial social, legal, cultural, and economic ‘nonideal primary goods’ for combating injustice, as well as grounds to distribute these goods in an equitable and inclusive manner. Finally, I illustrate how the model indexes the nonideal primary goods it justifies to different nonideal contexts and background normative frameworks, illustrating why diverse theorists should find the model and its output principles attractive. (shrink)