# Axiomatic Choice

arXiv: https://arxiv.org/abs/2509.21836 · HTML: https://mlsanity.com/paper/2509.21836/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback](https://mlsanity.com/paper/2404.10271/) (2024)
- [Axioms for AI Alignment from Human Feedback](https://mlsanity.com/paper/2405.14758/) (2024)
- [Enabling the Digital Democratic Revival: A Research Program for Digital Democracy](https://mlsanity.com/paper/2401.16863/) (2024)
- [Justifying Social-Choice Mechanism Outcome for Improving Participant Satisfaction](https://mlsanity.com/paper/2205.15863/) (2022)
- [Explainability in Mechanism Design: Recent Advances and the Road Ahead](https://mlsanity.com/paper/2206.03031/) (2022)

## Abstract (arXiv)

People care about decision outcomes and how decisions get made, both when making decisions and reflecting on decisions. But formalizing the full range of normative concerns that drive decisions is an open challenge. We introduce Axiomatic Choice as a framework for making and evaluating decisions based on formal normative statements about decisions. These statements, or axioms, capture a wide array of desiderata, e.g., ethical constraints, beyond the typical treatment in Social Choice. Using our model of axioms and decisions we define key properties and introduce a taxonomy of axioms which may be of general interest. We then use these properties and our taxonomy to define the Decision-Evaluation Paradox, formalize the concepts of transparency and deception in explaining and justifying decisions, and reveal the limits of existing methods using axioms to make decisions.