Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866911935102976000 |
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| author | Barrainkua, Ainhize Gordaliza, Paula Lozano, Jose A. Quadrianto, Novi |
| author_facet | Barrainkua, Ainhize Gordaliza, Paula Lozano, Jose A. Quadrianto, Novi |
| contents | This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage instances with uncertain identity with regards to the sensitive attribute to train a conventional machine learning classifier. The enhanced fairness observed in the final predictions of this classifier highlights the promising potential of prioritizing ambiguity (i.e., non-normativity) as a means to improve fairness guarantees in real-world classification tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19066 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers Barrainkua, Ainhize Gordaliza, Paula Lozano, Jose A. Quadrianto, Novi Machine Learning Computers and Society 68T01, 68T37 A.0; I.2 This paper introduces a novel approach to bolster algorithmic fairness in scenarios where sensitive information is only partially known. In particular, we propose to leverage instances with uncertain identity with regards to the sensitive attribute to train a conventional machine learning classifier. The enhanced fairness observed in the final predictions of this classifier highlights the promising potential of prioritizing ambiguity (i.e., non-normativity) as a means to improve fairness guarantees in real-world classification tasks. |
| title | Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers |
| topic | Machine Learning Computers and Society 68T01, 68T37 A.0; I.2 |
| url | https://arxiv.org/abs/2406.19066 |