Black box machine learning models can be dangerous for high-stakes decisions. They rely on untrustworthy databases, and their predictions are difficult to troubleshoot, explain and error check for real-time predictions. Their use leads to serious ethics and accountability issues.
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References
Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nat. Mach. Intell.1, 206–215 (2019).
Rudin, C. et al. Interpretable machine learning: fundamental principles and 10 grand challenges.Stat. Surv.16, 1–85 (2022).
Ledford, H. Millions of Black people affected by racial bias in health-care algorithms.Nature574, 608–609 (2019).
Kan-Tor, Y., Ben-Meir, A. & Buxboim, A. Can deep learning automatically predict fetal heart pregnancy with almost perfect accuracy?Hum. Reprod.35, 1473 (2020).
Badgeley, M. A. et al. Deep learning predicts hip fracture using confounding patient and healthcare variables.NPJ Digit. Med.2, 31 (2019).
弗洛雷斯,a·W。贝克特尔,k & Lowenkamp c t .歧视e positives, false negatives, and false analyses: a rejoinder to “Machine bias: there’s software used across the country to predict future criminals. And it’s biased against Blacks.”.Fed. Probat.80, 38–46 (2016).
Barnett, A. J. et al. A case-based interpretable deep learning model for classification of mass lesions in digital mammography.Nat. Mach. Intell.3, 1061–1070 (2021).
Rudin, C. & Radin, J. Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition.Harv. Data Sci. Rev.https://doi.org/10.1162/99608f92.5a8a3a3d(2019).
Afnan, M. A. M. et al. Ethical implementation of artificial intelligence to select embryos in in vitro fertilization. inAIES ’21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society316–326 (AIES, 2021).
Semenova, L., Rudin, C. & Parr, R. On the existence of simpler machine learning models. inFAccT ’22: 2022 ACM Conference on Fairness, Accountability and Transparency1827–1858 (ACM, 2022).
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Rudin, C. Why black box machine learning should be avoided for high-stakes decisions, in brief.Nat Rev Methods Primers2, 81 (2022). https://doi.org/10.1038/s43586-022-00172-0
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DOI:https://doi.org/10.1038/s43586-022-00172-0
