Placeholder to discuss the details of designing a algorithms that learn to complement humans.
- Lauren Oakden-Rayner, No Doctor Required: Autonomy, Anomalies, and Magic Puddings
- Machine learning for medical imaging: methodological failures and recommendations for the future
- Impact of artificial intelligence on pathologists’ decisions: an experiment
- Are Model Explanations Useful in Practice? Rethinking How to Support Human-ML Interactions.
- How to Make Tech Products (that Don't Cause Depression and War)
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.
Hohenstein, Jess, Rene F. Kizilcec, Dominic DiFranzo, Zhila Aghajari, Hannah Mieczkowski, Karen Levy, Mor Naaman, Jeffrey Hancock, and Malte F. Jung. 2023. “Artificial Intelligence in Communication Impacts Language and Social Relationships.” Scientific Reports 13 (1): 5487.
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.
No comments yet. Why not leave one?