On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910882176434176 |
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| author | Binkyte, Ruta Grozdanovski, Ljupcho Zhioua, Sami |
| author_facet | Binkyte, Ruta Grozdanovski, Ljupcho Zhioua, Sami |
| contents | As Artificial Intelligence (AI) increasingly influences decisions in critical societal sectors, understanding and establishing causality becomes essential for evaluating the fairness of automated systems. This article explores the significance of causal reasoning in addressing algorithmic discrimination, emphasizing both legal and societal perspectives. By reviewing landmark cases and regulatory frameworks, particularly within the European Union, we illustrate the challenges inherent in proving causal claims when confronted with opaque AI decision-making processes. The discussion outlines practical obstacles and methodological limitations in applying causal inference to real-world fairness scenarios, proposing actionable solutions to enhance transparency, accountability, and fairness in algorithm-driven decisions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_04053 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis Binkyte, Ruta Grozdanovski, Ljupcho Zhioua, Sami Machine Learning Artificial Intelligence Computers and Society As Artificial Intelligence (AI) increasingly influences decisions in critical societal sectors, understanding and establishing causality becomes essential for evaluating the fairness of automated systems. This article explores the significance of causal reasoning in addressing algorithmic discrimination, emphasizing both legal and societal perspectives. By reviewing landmark cases and regulatory frameworks, particularly within the European Union, we illustrate the challenges inherent in proving causal claims when confronted with opaque AI decision-making processes. The discussion outlines practical obstacles and methodological limitations in applying causal inference to real-world fairness scenarios, proposing actionable solutions to enhance transparency, accountability, and fairness in algorithm-driven decisions. |
| title | On the Need and Applicability of Causality for Fairness: A Unified Framework for AI Auditing and Legal Analysis |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2207.04053 |