Algorithmic Fairness: A Tolerance Perspective Article Swipe
YOU?
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· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2405.09543
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.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2405.09543
- https://arxiv.org/pdf/2405.09543
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396987692
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396987692Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2405.09543Digital Object Identifier
- Title
-
Algorithmic Fairness: A Tolerance PerspectiveWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-26Full publication date if available
- Authors
-
Renqiang Luo, Tao Tang, Feng Xia, Jiaying Liu, Chengpei Xu, Leo Yu Zhang, Wei Xiang, Chengqi ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2405.09543Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2405.09543Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2405.09543Direct OA link when available
- Concepts
-
Perspective (graphical), Computer science, Psychology, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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