mlsanityData to 14 Oct 2026

Pluralistic Alignment Over Time

arXiv 2411.10654 ↗First posted 16 Nov 2024cs.AI cs.CY cs.LGcited by 3

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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.

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

7 of 26 references.

References · 7

  1. 2020Reward Machines: Exploiting Reward Function Structure in Reinforcement Learningcited by 191
  2. 2024Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedbackcited by 100
  3. 2024Beyond Preferences in AI Alignmentcited by 49
  4. 2024AI Alignment with Changing and Influenceable Reward Functionscited by 33
  5. 2023Consistent Aggregation of Objectives with Diverse Time Preferences Requires Non-Markovian Rewardscited by 8
  6. 2023Remembering to Be Fair: Non-Markovian Fairness in Sequential Decision Makingcited by 8
  7. 2024Temporal Fairness in Decision Making Problemscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025AI Alignment at Your Discretioncited by 20
  2. 2026Principles Do Not Apply Themselves: A Hermeneutic Perspective on AI Alignmentcited by 2
  3. 2026Overton Pluralistic Reinforcement Learning for Large Language Modelscited by 1