[2] Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, and Christopher Ré. Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. In Proceedings of the ACM Conference on Health, Inference, and Learning, pages 151-159, 2020.
[3] Harini Suresh and John V Guttag. A framework for understanding unintended consequences of machine learning. arXiv preprint arXiv:1901.10002, 2:8, 2019.
[4] Ángel Alexander Cabrera, Will Epperson, Fred Hohman, Minsuk Kahng, Jamie Morgenstern, and Duen Horng Chau. FairVis: Visual analytics for discovering intersectional bias in machine learning. In 2019 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 46-56. IEEE, 2019.
[5] Angel Alexander Cabrera, Minsuk Kahng, Fred Hohman, Jamie Morgenstern, and Duen Horng Chau. Discovery of intersectional bias in machine learning using automatic subgroup generation. In ICLR Debugging Machine Learning Models Workshop, 2019.
[6] Oleg S Pianykh, Georg Langs, Marc Dewey, Dieter R Enzmann, Christian J Herold, Stefan O Schoenberg, and James A Brink. Continuous learning AI in radiology: Implementation principles and early applications. Radiology, 297(1):6-14, 2020.
[7] Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence. Dataset shift in machine learning. MIT Press, 2008.
[8] Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang. WILDS: A benchmark of in-the-wild distribution shifts. In International Conference on Machine Learning, pages 5637-5664. PMLR, 2021.
[9] Nabeel Seedat, Fergus Imrie, and Mihaela van der Schaar. Dc-check: A data-centric ai checklist to guide the development of reliable machine learning systems. arXiv preprint arXiv:2211.05764, 2022.
[10] Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Iii, and Kate Crawford. Datasheets for datasets. Communications of the ACM, 64(12):86-92, 2021.
[11] Harini Suresh, Jen J Gong, and John V Guttag. Learning tasks for multitask learning: Heterogeneous patient populations in the ICU. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 802-810, 2018.
[12] Karan Goel, Albert Gu, Yixuan Li, and Christopher Re. Model patching: Closing the subgroup performance gap with data augmentation. In International Conference on Learning Representations, 2020.
[13] Suchi Saria and Adarsh Subbaswamy. Tutorial: safe and reliable machine learning. arXiv preprint arXiv:1904.07204, 2019.
[14] Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D Hoffman, et al. Under-specification presents challenges for credibility in modern machine learning. arXiv preprint arXiv:2011.03395, 2020.
[15] Joseph Paul Cohen, Tianshi Cao, Joseph D Viviano, Chin-Wei Huang, Michael Fralick, Marzyeh Ghassemi, Muhammad Mamdani, Russell Greiner, and Yoshua Bengio. Problems in the deployment of machine-learned models in health care. CMAJ, 193(35):E1391-E1394, 2021.
[16] Rahul C Deo. Machine learning in medicine. Circulation, 132(20):1920-1930, 2015.
[17] Neil Savage. Better medicine through machine learning. Communications of the ACM, 55(1):17-19, 2012.