Algorithmic Fairness: A Tolerance Perspective
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911877408227328 |
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| author | Luo, Renqiang Tang, Tao Xia, Feng Liu, Jiaying Xu, Chengpei Zhang, Leo Yu Xiang, Wei Zhang, Chengqi |
| author_facet | Luo, Renqiang Tang, Tao Xia, Feng Liu, Jiaying Xu, Chengpei Zhang, Leo Yu Xiang, Wei Zhang, Chengqi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09543 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Algorithmic Fairness: A Tolerance Perspective Luo, Renqiang Tang, Tao Xia, Feng Liu, Jiaying Xu, Chengpei Zhang, Leo Yu Xiang, Wei Zhang, Chengqi Computers and Society Artificial Intelligence Information Retrieval Machine Learning 68T01, 68W40 I.2.6; K.4.2; H.1.2 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. |
| title | Algorithmic Fairness: A Tolerance Perspective |
| topic | Computers and Society Artificial Intelligence Information Retrieval Machine Learning 68T01, 68W40 I.2.6; K.4.2; H.1.2 |
| url | https://arxiv.org/abs/2405.09543 |