Masayoshi Mase, Art B. Owen, Benjamin B. Seiler
arXiv 15 May 2021 · Machine Learning · 3 citations (OpenAlex)
arXiv:2105.07168 · PDF · DOI · OpenAlex · Extracted main text
Cohort Shapley value is a model-free method of variable importance grounded in game theory that does not use any unobserved and potentially impossible feature combinations. We use it to evaluate algorithmic fairness, using the well known COMPAS recidivism data as our example. This approach allows one to identify for each individual in a data set the extent to which they were adversely or beneficially affected by their value of a protected attribute such as their race. The method can do this even if race was not one of the original predictors and even if it does not have access to a proprietary algorithm that has made the predictions. The grounding in game theory lets us define aggregate variable importance for a data set consistently with its per subject definitions. We can investigate variable importance for multiple quantities of interest in the fairness literature including false positive predictions.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Mase, M., Owen, A. B., and Seiler, B. B (2019) Explaining black box decisions by Shapley cohort refinement self | 0.928 | 4 | 3 | 100% |
| 2 | Angwin, J., Larson, J., Mattu, S., and Kirchner, L (2016) Machine bias: there’s software used across the country to predict future criminals. and it’s biased against blacks | 0.874 | 5 | 2 | 100% |
| 3 | Chouldechova, A (2017) Fair prediction with disparate impact: A study of bias in recidivism prediction instruments | 0.811 | 4 | 2 | 100% |
| 4 | Lundberg, S. M. and Lee, S.-I (2017) A unified approach to interpreting model predictions | 0.644 | 2 | 2 | 100% |
| 5 | Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgon… (2021) The future of sensitivity analysis: An essential discipline for systems modeling and policy support | 0.644 | 2 | 2 | 100% |
| 6 | Shapley, L. S (1953) A value for n-person games | 0.644 | 2 | 2 | 100% |
| 7 | Sundararajan, M. and Najmi, A (2020) The many Shapley values for model explanation | 0.585 | 3 | 1 | 100% |
| 8 | Flores, A. W., Bechtel, K., and Lowenkamp, C. T (2016) False positives, false negatives, and false analyses: A rejoinder to machine bias: There's software used across the country to p… | 0.511 | 2 | 1 | 100% |
| 9 | Adler, P., Falk, C., Friedler, S. A., Nix, T., Rybeck, G., Scheidegg… (2018) Auditing black-box models for indirect influence | 0.405 | 1 | 1 | 100% |
| 10 | Berk, R., Heidari, H., Jabbari, S., Kearns, M., and Roth, A (2018) Fairness in criminal justice risk assessments: The state of the art | 0.405 | 1 | 1 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Variable importance without impossible data | 0.511 | 2 | 1 |