Distribution-Free Statistical Dispersion Control for Societal Applications
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917605320687616 |
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| author | Deng, Zhun Zollo, Thomas P. Snell, Jake C. Pitassi, Toniann Zemel, Richard |
| author_facet | Deng, Zhun Zollo, Thomas P. Snell, Jake C. Pitassi, Toniann Zemel, Richard |
| contents | Explicit finite-sample statistical guarantees on model performance are an important ingredient in responsible machine learning. Previous work has focused mainly on bounding either the expected loss of a predictor or the probability that an individual prediction will incur a loss value in a specified range. However, for many high-stakes applications, it is crucial to understand and control the dispersion of a loss distribution, or the extent to which different members of a population experience unequal effects of algorithmic decisions. We initiate the study of distribution-free control of statistical dispersion measures with societal implications and propose a simple yet flexible framework that allows us to handle a much richer class of statistical functionals beyond previous work. Our methods are verified through experiments in toxic comment detection, medical imaging, and film recommendation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_13786 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Distribution-Free Statistical Dispersion Control for Societal Applications Deng, Zhun Zollo, Thomas P. Snell, Jake C. Pitassi, Toniann Zemel, Richard Machine Learning Explicit finite-sample statistical guarantees on model performance are an important ingredient in responsible machine learning. Previous work has focused mainly on bounding either the expected loss of a predictor or the probability that an individual prediction will incur a loss value in a specified range. However, for many high-stakes applications, it is crucial to understand and control the dispersion of a loss distribution, or the extent to which different members of a population experience unequal effects of algorithmic decisions. We initiate the study of distribution-free control of statistical dispersion measures with societal implications and propose a simple yet flexible framework that allows us to handle a much richer class of statistical functionals beyond previous work. Our methods are verified through experiments in toxic comment detection, medical imaging, and film recommendation. |
| title | Distribution-Free Statistical Dispersion Control for Societal Applications |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2309.13786 |