How fair can we go in machine learning? Assessing the boundaries of accuracy and fairness
Fair machine learning has been focusing on the development of equitable algorithms that address discrimination. Yet, many of these fairness-aware approaches aim to obtain a unique solution to the problem, which leads to a poor understanding of the statistical limits of bias mitigation interventions....
| 主要な著者: | , , |
|---|---|
| フォーマット: | Journal article |
| 言語: | English |
| 出版事項: |
Wiley
2021
|