From traditional to distributed responsibility: moral responsibility in generative artificial intelligence

Abstract

The increasing application of generative artificial intelligence (GAI) in social life has brought convenience and efficiency to humanity, while also giving rise to ethical dilemmas concerning security and trust, as well as challenges to moral responsibility. Moreover, the new patterns of human-machine interaction derived from this technology are challenging traditional models. GAI systems, comprising humans, artificial entities, and human-machine hybrids, function as multi-agent systems that meet the requirements of distributed morality. Distributed morality and its mechanisms for attributing responsibility determine the scope of moral responsibility in a recipient-oriented approach, appropriately attribute responsibility through causal analysis by tracing causal relations backward, and seek to guide the system as a whole towards the greater good by utilizing the cyclical feedback mechanisms of the system. Although distributed moral responsibility faces challenges in terms of fairness and social perception, it offers greater explanatory power and feasibility than traditional models of moral responsibility attribution, thereby opening up new governance pathways for addressing the ethical and moral challenges faced by GAI in its application and future development.