Moral Uncertainty and the Problem of Fanaticism
This addresses a foundational issue in moral uncertainty for philosophers and AI ethics, offering incremental improvements to existing aggregation approaches.
The paper tackles the problem of fanaticism in moral uncertainty, where a single ethical theory can dominate decisions despite low confidence, by formalizing it mathematically and proposing non-fanatical aggregation methods like Highest k-trimmed Mean and Highest Median.
While there is universal agreement that agents ought to act ethically, there is no agreement as to what constitutes ethical behaviour. To address this problem, recent philosophical approaches to `moral uncertainty' propose aggregation of multiple ethical theories to guide agent behaviour. However, one of the foundational proposals for aggregation - Maximising Expected Choiceworthiness (MEC) - has been criticised as being vulnerable to fanaticism; the problem of an ethical theory dominating agent behaviour despite low credence (confidence) in said theory. Fanaticism thus undermines the `democratic' motivation for accommodating multiple ethical perspectives. The problem of fanaticism has not yet been mathematically defined. Representing moral uncertainty as an instance of social welfare aggregation, this paper contributes to the field of moral uncertainty by 1) formalising the problem of fanaticism as a property of social welfare functionals and 2) providing non-fanatical alternatives to MEC, i.e. Highest k-trimmed Mean and Highest Median.