# Pluralistic Alignment Over Time

arXiv: https://arxiv.org/abs/2411.10654 · HTML: https://mlsanity.com/paper/2411.10654/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 7 of 7)

- [Reward Machines: Exploiting Reward Function Structure in Reinforcement Learning](https://mlsanity.com/paper/2010.03950/) (2020)
- [Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback](https://mlsanity.com/paper/2404.10271/) (2024)
- [Beyond Preferences in AI Alignment](https://mlsanity.com/paper/2408.16984/) (2024)
- [AI Alignment with Changing and Influenceable Reward Functions](https://mlsanity.com/paper/2405.17713/) (2024)
- [Consistent Aggregation of Objectives with Diverse Time Preferences Requires Non-Markovian Rewards](https://mlsanity.com/paper/2310.00435/) (2023)
- [Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Making](https://mlsanity.com/paper/2312.04772/) (2023)
- [Temporal Fairness in Decision Making Problems](https://mlsanity.com/paper/2408.13208/) (2024)

## Built on by (most cited first, 3 of 3)

- [AI Alignment at Your Discretion](https://mlsanity.com/paper/2502.10441/) (2025)
- [Principles Do Not Apply Themselves: A Hermeneutic Perspective on AI Alignment](https://mlsanity.com/paper/2604.10673/) (2026)
- [Overton Pluralistic Reinforcement Learning for Large Language Models](https://mlsanity.com/paper/2602.20759/) (2026)

## Abstract (arXiv)

If an AI system makes decisions over time, how should we evaluate how aligned it is with a group of stakeholders (who may have conflicting values and preferences)? In this position paper, we advocate for consideration of temporal aspects including stakeholders' changing levels of satisfaction and their possibly temporally extended preferences. We suggest how a recent approach to evaluating fairness over time could be applied to a new form of pluralistic alignment: temporal pluralism, where the AI system reflects different stakeholders' values at different times.