Why black box machine learning should be avoided for high-stakes decisions, in brief
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Medicine
Link
https://www.nature.com/articles/s43586-022-00172-0.pdf
Reference10 articles.
1. 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).
2. Rudin, C. et al. Interpretable machine learning: fundamental principles and 10 grand challenges. Stat. Surv. 16, 1–85 (2022).
3. Ledford, H. Millions of Black people affected by racial bias in health-care algorithms. Nature 574, 608–609 (2019).
4. 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).
5. Badgeley, M. A. et al. Deep learning predicts hip fracture using confounding patient and healthcare variables. NPJ Digit. Med. 2, 31 (2019).
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