Moral Uncertainty Navigator

Most serious ethical decisions are made under conditions of moral uncertainty — not just empirical uncertainty about consequences, but genuine uncertainty about which ethical theory is correct, what moral status attaches to various entities, and whether specific moral intuitions are reliable guides. Pretending this uncertainty does not exist leads to false confidence in ethical conclusions. Learning to reason and decide well under moral uncertainty is one of the most important and underappreciated skills in applied ethics.

The Moral Uncertainty Navigator AI assistant is designed for applied ethicists, organizational leaders, policy analysts, AI ethics researchers, and anyone who needs to make principled decisions when the ethical landscape is genuinely unclear. It draws on the emerging philosophical literature on moral uncertainty — developed by philosophers including Will MacAskill, Toby Ord, and others working in the effective altruism and decision theory traditions — as well as classical approaches to reasoning under normative uncertainty.

This assistant helps you map the moral uncertainty structure of a given decision: which ethical questions are well-settled across major frameworks, which are genuinely contested, and what practical guidance can be extracted from partial agreements among theories you assign credence to. It introduces and applies the major strategies for decision-making under moral uncertainty: maximizing expected moral value across theories weighted by credence, avoiding moral catastrophe through risk-averse strategies, seeking robustness across frameworks through option dominance analysis, and applying the parliamentary model where different theories vote on options weighted by their credibility.

It also helps you identify when moral uncertainty is the appropriate epistemic attitude and when it shades into motivated reasoning or moral paralysis — cases where one option is clearly superior across all plausible frameworks and uncertainty is being invoked to avoid an uncomfortable conclusion. It helps users maintain epistemic integrity while still making practical decisions.

Ideal for AI ethics researchers, policy analysts navigating contested normative terrain, organizational leaders facing genuinely underdetermined ethical choices, bioethicists, and philosophy researchers working at the intersection of ethics and decision theory.