arXiv 17 Aug 2021 · Statistics — Machine Learning · publishedMachine Learning (2022) · 1 citations (OpenAlex)
arXiv:2108.07380 · PDF · DOI · OpenAlex · Extracted main text
We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fair, transparent and trustworthy ML methods. The purpose of this article is to introduce a new information-theoretic learning framework (admissible machine learning) and algorithmic risk-management tools (InfoGram, L-features, ALFA-testing) that can guide an analyst to redesign off-the-shelf ML methods to be regulatory compliant, while maintaining good prediction accuracy. We have illustrated our approach using several real-data examples from financial sectors, biomedical research, marketing campaigns, and the criminal justice system.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Mukhopadhyay, S. and K. Wang (2020) Breiman’s “Two Cultures” revisited and reconciled self | 0.405 | 1 | 1 | 100% |
| 2 | Allen, B., S. Agarwal, L. Coombs, C. Wald, and K. Dreyer (2021) 2020 ACR Data Science Institute Artificial Intelligence Survey | 0.405 | 1 | 1 | 100% |
| 3 | Berrett, T. B., Y. Wang, R. F. Barber, and R. J. Samworth (2019) The conditional permutation test for independence while controlling for confounders | 0.405 | 1 | 1 | 100% |
| 4 | Blattner, L. and S. Nelson (2021) How costly is noise? Data and disparities in consumer credit | 0.405 | 1 | 1 | 100% |
| 5 | Breiman, L. et al (2004) Population theory for boosting ensembles | 0.405 | 1 | 1 | 100% |
| 6 | Brennan, T., W. Dieterich, and B. Ehret (2009) Evaluating the predictive validity of the compas risk and needs assessment system | 0.405 | 1 | 1 | 100% |
| 7 | Candes, E., Y. Fan, L. Janson, and J. Lv (2018) Panning for gold: ‘model-x’ knockoffs for high dimensional controlled variable selection | 0.405 | 1 | 1 | 100% |
| 8 | Fahner, G (2018) Developing transparent credit risk scorecards more effectively: An explainable artificial intelligence approach | 0.405 | 1 | 1 | 100% |
| 9 | Friedman, J. H (2001) Greedy function approximation: a gradient boosting machine | 0.405 | 1 | 1 | 100% |
| 10 | Friedman, J., T. Hastie, and R. Tibshirani (2010) Regularization paths for generalized linear models via coordinate descent | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 27 scored citations.