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Algorithmic Fairness Over Time: Advances & Prospects

arXiv 2610.15015 ↗First posted 10 Oct 2026cs.LGcited by 0

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Abstract (arXiv)

In the presence of feedback loops, algorithmic decisions at one time step can influence observed data in subsequent steps, potentially introducing selection bias or shifts in data distribution. Research has demonstrated that ignoring these effects can lead to unfairness, which may worsen over time as the model remains deployed within society. This raises important questions of how to measure and ensure fairness of algorithmic decision-making models over time. In this short paper, we present a taxonomy that distinguishes between different types of static and temporal fairness notions in recent advancements. We then suggest prospects for future research by identifying common implicit temporal assumptions, open questions to enhance our temporal understanding of fairness, and other open challenges.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 44 of its 71 references that are in the map. First posted on arXiv 10 Oct 2026.

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References · 44

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32 more
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