Placeholder to discuss the details of designing a algorithms that learn to complement humans.
- No Doctor Required: Autonomy, Anomalies, and Magic Puddings – Lauren Oakden-Rayner
- Machine learning for medical imaging: methodological failures and recommendations for the future | npj Digital Medicine
- Impact of artificial intelligence on pathologists’ decisions: an experiment | Journal of the American Medical Informatics Association | Oxford Academic
References
Agarwal, Chirag, Daniel D’souza, and Sara Hooker. 2021. “Estimating Example Difficulty Using Variance of Gradients.” arXiv:2008.11600 [Cs], September.
Charusaie, Mohammad-Amin, Hussein Mozannar, David Sontag, and Samira Samadi. 2022. “Sample Efficient Learning of Predictors That Complement Humans.” In Proceedings of the 39th International Conference on Machine Learning, 2972–3005. PMLR.
Fügener, Andreas, Jörn Grahl, Alok Gupta, and Wolfgang Ketter. 2021. “Will Humans-in-the-Loop Become Borgs? Merits and Pitfalls of Working with AI.” MIS Quarterly 45 (3): 1527–56.
Hilgard, Sophie, Nir Rosenfeld, Mahzarin R. Banaji, Jack Cao, and David C. Parkes. 2020. “Learning Representations by Humans, for Humans.” arXiv:1905.12686 [Cs, Stat], October.
Meyer, Julien, April Khademi, Bernard Têtu, Wencui Han, Pria Nippak, and David Remisch. 2022. “Impact of Artificial Intelligence on Pathologists’ Decisions: An Experiment.” Journal of the American Medical Informatics Association, June, ocac103.
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