Solving moral dilemmas with AI: how it helps us address the social implications of the Covid-19 crisis and enhance human responsibility to tackle meta-dilemmas

When combined with an appropriate level of human judgement, machine learning applications were crucial resources insupporting decision-making in the context of the Covid-19 crisis, resulting in more efficient and better-informed responses to ethicalissues. This paper focusses on four social dimensions (bioethical, political, psychological, and economic) from which the decisionstaken in the context of the Covid-19 crisis derived major ethical implications. On the one hand, I argue against the possibility ofaddressing these issues from a purely algorithmic approach, elaborating on two types of limitations for automated systems toaddress ethical issues. This leads me to discuss how different ethical situations call for different performance metrics with regards tothe ‘contextual explicability and performance issue’, as well as to enunciate a gold principle: ‘legitimacy trumps accuracy’. On the otherhand, I present practical examples of machine learning applications which enhance, instead of dilute, human moral agency in betteraddressing these issues. I also suggest a ‘moral perimeter’ framework to ensure the responsibility of algorithms-assisted decisionmakersfor critical decisions. The unique potential of AI to ‘solve’ moral dilemmas by intervening on their conditions of possibility thenprompts me to discuss a new type of moral situation: AI-generated meta-dilemmas.