# Moral Uncertainty and the Problem of Fanaticism

arXiv: https://arxiv.org/abs/2312.11589 · HTML: https://mlsanity.com/paper/2312.11589/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Reinforcement Learning Under Moral Uncertainty](https://mlsanity.com/paper/2006.04734/) (2020)

## Built on by (most cited first, 1 of 1)

- [Accounting for Context: Shaping Moral Credences for Value Alignment](https://mlsanity.com/paper/2606.06972/) (2026)

## Abstract (arXiv)

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.