Interventions Against Machine-Assisted Statistical Discrimination
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866916799634735104 |
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| author | Zhu, John Y. |
| author_facet | Zhu, John Y. |
| contents | I study statistical discrimination driven by verifiable beliefs, such as those generated by machine learning, rather than by humans. When beliefs are verifiable, interventions against statistical discrimination can move beyond simple, belief-free designs like affirmative action, to more sophisticated ones, that constrain decision makers based on what they are thinking. I design a belief-contingent intervention I call common identity. I show that it is effective at eliminating equilibrium statistical discrimination, even when training data exhibit the various statistical biases that often plague algorithmic decision problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_04585 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Interventions Against Machine-Assisted Statistical Discrimination Zhu, John Y. Theoretical Economics Machine Learning I study statistical discrimination driven by verifiable beliefs, such as those generated by machine learning, rather than by humans. When beliefs are verifiable, interventions against statistical discrimination can move beyond simple, belief-free designs like affirmative action, to more sophisticated ones, that constrain decision makers based on what they are thinking. I design a belief-contingent intervention I call common identity. I show that it is effective at eliminating equilibrium statistical discrimination, even when training data exhibit the various statistical biases that often plague algorithmic decision problems. |
| title | Interventions Against Machine-Assisted Statistical Discrimination |
| topic | Theoretical Economics Machine Learning |
| url | https://arxiv.org/abs/2310.04585 |