# Operationalising Rawlsian Ethics for Fairness in Norm-Learning Agents

arXiv: https://arxiv.org/abs/2412.15163 · HTML: https://mlsanity.com/paper/2412.15163/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 47 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2012.09421/) (2020)
- [A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings](https://mlsanity.com/paper/2106.09012/) (2021)
- [Taking Principles Seriously: A Hybrid Approach to Value Alignment](https://mlsanity.com/paper/2012.11705/) (2020)
- [Learning and Sustaining Shared Normative Systems via Bayesian Rule Induction in Markov Games](https://mlsanity.com/paper/2402.13399/) (2024)
- [Cooperative Multi-Agent Fairness and Equivariant Policies](https://mlsanity.com/paper/2106.05727/) (2021)
- [Macro Ethics Principles for Responsible AI Systems: Taxonomy and Future Directions](https://mlsanity.com/paper/2208.12616/) (2022)
- [The emergence of division of labor through decentralized social sanctioning](https://mlsanity.com/paper/2208.05568/) (2022)
- [Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities](https://mlsanity.com/paper/2301.05753/) (2023)
- [The Myth of Complete AI-Fairness](https://mlsanity.com/paper/2104.12544/) (2021)
- [SCALES: From Fairness Principles to Constrained Decision-Making](https://mlsanity.com/paper/2209.10860/) (2022)

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

- [Adaptive Accountability in Networked MAS: Tracing and Mitigating Emergent Norms at Scale](https://mlsanity.com/paper/2512.18561/) (2025)

## Abstract (arXiv)

Social norms are standards of behaviour common in a society. However, when agents make decisions without considering how others are impacted, norms can emerge that lead to the subjugation of certain agents. We present RAWL-E, a method to create ethical norm-learning agents. RAWL-E agents operationalise maximin, a fairness principle from Rawlsian ethics, in their decision-making processes to promote ethical norms by balancing societal well-being with individual goals. We evaluate RAWL-E agents in simulated harvesting scenarios. We find that norms emerging in RAWL-E agent societies enhance social welfare, fairness, and robustness, and yield higher minimum experience compared to those that emerge in agent societies that do not implement Rawlsian ethics.