AI Fairness in Practice
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913276966731776 |
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| author | Leslie, David Rincon, Cami Briggs, Morgan Perini, Antonella Jayadeva, Smera Borda, Ann Bennett, SJ Burr, Christopher Aitken, Mhairi Katell, Michael Fischer, Claudia Wong, Janis Garcia, Ismael Kherroubi |
| author_facet | Leslie, David Rincon, Cami Briggs, Morgan Perini, Antonella Jayadeva, Smera Borda, Ann Bennett, SJ Burr, Christopher Aitken, Mhairi Katell, Michael Fischer, Claudia Wong, Janis Garcia, Ismael Kherroubi |
| contents | Reaching consensus on a commonly accepted definition of AI Fairness has long been a central challenge in AI ethics and governance. There is a broad spectrum of views across society on what the concept of fairness means and how it should best be put to practice. In this workbook, we tackle this challenge by exploring how a context-based and society-centred approach to understanding AI Fairness can help project teams better identify, mitigate, and manage the many ways that unfair bias and discrimination can crop up across the AI project workflow.
We begin by exploring how, despite the plurality of understandings about the meaning of fairness, priorities of equality and non-discrimination have come to constitute the broadly accepted core of its application as a practical principle. We focus on how these priorities manifest in the form of equal protection from direct and indirect discrimination and from discriminatory harassment. These elements form ethical and legal criteria based upon which instances of unfair bias and discrimination can be identified and mitigated across the AI project workflow.
We then take a deeper dive into how the different contexts of the AI project lifecycle give rise to different fairness concerns. This allows us to identify several types of AI Fairness (Data Fairness, Application Fairness, Model Design and Development Fairness, Metric-Based Fairness, System Implementation Fairness, and Ecosystem Fairness) that form the basis of a multi-lens approach to bias identification, mitigation, and management. Building on this, we discuss how to put the principle of AI Fairness into practice across the AI project workflow through Bias Self-Assessment and Bias Risk Management as well as through the documentation of metric-based fairness criteria in a Fairness Position Statement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14636 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI Fairness in Practice Leslie, David Rincon, Cami Briggs, Morgan Perini, Antonella Jayadeva, Smera Borda, Ann Bennett, SJ Burr, Christopher Aitken, Mhairi Katell, Michael Fischer, Claudia Wong, Janis Garcia, Ismael Kherroubi Computers and Society Artificial Intelligence Human-Computer Interaction Reaching consensus on a commonly accepted definition of AI Fairness has long been a central challenge in AI ethics and governance. There is a broad spectrum of views across society on what the concept of fairness means and how it should best be put to practice. In this workbook, we tackle this challenge by exploring how a context-based and society-centred approach to understanding AI Fairness can help project teams better identify, mitigate, and manage the many ways that unfair bias and discrimination can crop up across the AI project workflow. We begin by exploring how, despite the plurality of understandings about the meaning of fairness, priorities of equality and non-discrimination have come to constitute the broadly accepted core of its application as a practical principle. We focus on how these priorities manifest in the form of equal protection from direct and indirect discrimination and from discriminatory harassment. These elements form ethical and legal criteria based upon which instances of unfair bias and discrimination can be identified and mitigated across the AI project workflow. We then take a deeper dive into how the different contexts of the AI project lifecycle give rise to different fairness concerns. This allows us to identify several types of AI Fairness (Data Fairness, Application Fairness, Model Design and Development Fairness, Metric-Based Fairness, System Implementation Fairness, and Ecosystem Fairness) that form the basis of a multi-lens approach to bias identification, mitigation, and management. Building on this, we discuss how to put the principle of AI Fairness into practice across the AI project workflow through Bias Self-Assessment and Bias Risk Management as well as through the documentation of metric-based fairness criteria in a Fairness Position Statement. |
| title | AI Fairness in Practice |
| topic | Computers and Society Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2403.14636 |