Zhao, Kino (2026) Is algorithmic bias a data problem? In: UNSPECIFIED.
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v.4.1 bias in bias out.docx Download (70kB) |
Abstract
Discussions of algorithmic bias often assume that biases in predictive outcomes reflect biases in society. That is, machine learning algorithms are biased because they are preserve bias from training data. This paper challenges this assumption by pointing to a tension between the philosophy of data literature, which largely rejects the idea that data possess essential features that are always preserved in analysis, and the claim that social biases are easily preservable despite attempts at getting rid of them.
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| Item Type: | Conference or Workshop Item (UNSPECIFIED) | ||||||
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| Keywords: | Algorithmic bias, data, machine learning | ||||||
| Subjects: | General Issues > Data Specific Sciences > Artificial Intelligence > Machine Learning |
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| Depositing User: | Dr. Kino Zhao | ||||||
| Date Deposited: | 29 May 2026 12:35 | ||||||
| Last Modified: | 29 May 2026 12:35 | ||||||
| Item ID: | 29784 | ||||||
| Subjects: | General Issues > Data Specific Sciences > Artificial Intelligence > Machine Learning |
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| Date: | 28 May 2026 | ||||||
| URI: | https://philsci-archive.pitt.edu/id/eprint/29784 |
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