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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.04534 |
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| _version_ | 1866916250989363200 |
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| author | Montaseri, Hamidreza Gohari, Amin |
| author_facet | Montaseri, Hamidreza Gohari, Amin |
| contents | The proliferation of algorithmic systems has fueled discussions surrounding the regulation and control of their social impact. Herein, we consider a system whose primary objective is to maximize utility by selecting the most qualified individuals. To promote demographic parity in the selection algorithm, we consider penalizing discrimination across social groups. We examine conditions under which a discrimination penalty can effectively reduce disparity in the selection. Additionally, we explore the implications of such a penalty when individual qualifications may evolve over time in response to the imposed penalizing policy. We identify scenarios where the penalty could hinder the natural attainment of equity within the population. Moreover, we propose certain conditions that can counteract this undesirable outcome, thus ensuring fairness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04534 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Impact of Fairness Regulations on Institutions' Policies and Population Qualifications Montaseri, Hamidreza Gohari, Amin Machine Learning Artificial Intelligence Computers and Society The proliferation of algorithmic systems has fueled discussions surrounding the regulation and control of their social impact. Herein, we consider a system whose primary objective is to maximize utility by selecting the most qualified individuals. To promote demographic parity in the selection algorithm, we consider penalizing discrimination across social groups. We examine conditions under which a discrimination penalty can effectively reduce disparity in the selection. Additionally, we explore the implications of such a penalty when individual qualifications may evolve over time in response to the imposed penalizing policy. We identify scenarios where the penalty could hinder the natural attainment of equity within the population. Moreover, we propose certain conditions that can counteract this undesirable outcome, thus ensuring fairness. |
| title | Impact of Fairness Regulations on Institutions' Policies and Population Qualifications |
| topic | Machine Learning Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2404.04534 |