Algorithmic Fairness: A Tolerance Perspective
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Abstract (arXiv)
Recent advancements in machine learning and deep learning have brought algorithmic fairness into sharp focus, illuminating concerns over discriminatory decision making that negatively impacts certain individuals or groups. These concerns have manifested in legal, ethical, and societal challenges, including the erosion of trust in intelligent systems. In response, this survey delves into the existing literature on algorithmic fairness, specifically highlighting its multifaceted social consequences. We introduce a novel taxonomy based on 'tolerance', a term we define as the degree to which variations in fairness outcomes are acceptable, providing a structured approach to understanding the subtleties of fairness within algorithmic decisions. Our systematic review covers diverse industries, revealing critical insights into the balance between algorithmic decision making and social equity. By synthesizing these insights, we outline a series of emerging challenges and propose strategic directions for future research and policy making, with the goal of advancing the field towards more equitable algorithmic systems.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 73 of its 200 references that are in the map. First posted on arXiv 26 Apr 2024.
Builds on
73 of 200 references.
References · 73
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- 2021Causal Intervention for Leveraging Popularity Bias in Recommendationcited by 116
- 2022A Survey on the Fairness of Recommender Systemscited by 102
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- 2020Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problemcited by 86
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- 2019Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combinationcited by 79
- 2022Fairness in Graph Mining: A Surveycited by 76
- 2019Policy Learning for Fairness in Rankingcited by 74
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- 2019Degenerate Feedback Loops in Recommender Systemscited by 67
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- 2019The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metriccited by 41
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