Abstract

Recent academic discourse about artificial intelligence (AI) has largely been directed at how to best morally program AI or evaluating the ethics of its use in various contexts. While these efforts are undoubtedly important, this essay proposes a complementary objective: deploying AI to enhance our own ethical conduct. One way we might do this is by using AI to deepen our understanding of human moral psychology. In this paper, I demonstrate how advanced machine learning might help us gain clearer insights into “common sense” morality—shared moral convictions that underpin our reflective judgments and inform central aspects of moral philosophy. Pinpointing such convictions has proven challenging amid widespread moral disagreements. Current approaches to understanding these commitments, although exhibiting some key strengths, ultimately struggle to capture relevant features of reflective moral judgments espoused by John Rawls, leaving room for methodological improvement. Modern advances in AI offer a promising opportunity to make progress on this task. This essay envisions the gamified training of a “collective moral conscience model,” able to render judgments about moral situations that align with the deep-seated principles of the human collective. I argue that such an AI model might make progress in overcoming obstacles of disagreement to aid in philosophical theorizing and foster practical applications for the moral life of AI agents and ourselves, such as offering us guidance in time-constrained dilemmas and helping us to reflect on our own biases.