| A framework for understanding sources of harm throughout the machine learning life cycle H Suresh, J Guttag Proceedings of the 1st ACM Conference on Equity and Access in Algorithms …, 2021 | 1645* | 2021 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research 23 (226), 1-61, 2022 | 1027 | 2022 |
| Do as AI say: susceptibility in deployment of clinical decision-aids S Gaube, H Suresh, M Raue, A Merritt, SJ Berkowitz, E Lermer, ... NPJ digital medicine 4 (1), 31, 2021 | 472 | 2021 |
| Clinical intervention prediction and understanding with deep neural networks H Suresh, N Hunt, A Johnson, LA Celi, P Szolovits, M Ghassemi Machine learning for healthcare conference, 322-337, 2017 | 331* | 2017 |
| Beyond expertise and roles: A framework to characterize the stakeholders of interpretable machine learning and their needs H Suresh, SR Gomez, KK Nam, A Satyanarayan Proceedings of the 2021 CHI conference on human factors in computing systems …, 2021 | 190 | 2021 |
| Learning tasks for multitask learning: Heterogenous patient populations in the icu H Suresh, JJ Gong, JV Guttag Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018 | 127 | 2018 |
| Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays S Gaube, H Suresh, M Raue, E Lermer, TK Koch, MFC Hudecek, ... Scientific reports 13 (1), 1383, 2023 | 109 | 2023 |
| Towards intersectional feminist and participatory ML: a case study in supporting feminicide counterdata collection H Suresh, R Movva, AL Dogan, R Bhargava, I Cruxen, A Martinez Cuba, ... 2022 ACM Conference on Fairness, Accountability, and Transparency, 667-678, 2022 | 90* | 2022 |
| Misplaced trust: Measuring the interference of machine learning in human decision-making H Suresh, N Lao, I Liccardi Proceedings of the 12th ACM conference on web science, 315-324, 2020 | 90 | 2020 |
| Approximate communication: Techniques for reducing communication bottlenecks in large-scale parallel systems F Betzel, K Khatamifard, H Suresh, DJ Lilja, J Sartori, U Karpuzcu ACM Computing Surveys (CSUR) 51 (1), 1-32, 2018 | 79 | 2018 |
| Participation in the age of foundation models H Suresh, E Tseng, M Young, M Gray, E Pierson, K Levy Proceedings of the 2024 ACM Conference on Fairness, Accountability, and …, 2024 | 64 | 2024 |
| Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients RC Lacson, B Baker, H Suresh, K Andriole, P Szolovits, E Lacson Jr Clinical kidney journal 12 (2), 206-212, 2019 | 57 | 2019 |
| Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions … JY Kim, A Hasan, KC Kellogg, W Ratliff, SG Murray, H Suresh, ... PLOS Digital Health 3 (5), e0000390, 2024 | 50 | 2024 |
| Feminicide and counterdata production: Activist efforts to monitor and challenge gender-related violence C D'Ignazio, I Cruxên, HS Val, AM Cuba, M García-Montes, S Fumega, ... Patterns 3 (7), 2022 | 50 | 2022 |
| Orientation-specific attachment of polymeric microtubes on cell surfaces JB Gilbert, JS O'Brien, HS Suresh, RE Cohen, MF Rubner WILEY-VCH Verlag GmbH & Co., 2013 | 49 | 2013 |
| Semi-supervised biomedical translation with cycle wasserstein regression GANs M McDermott, T Yan, T Naumann, N Hunt, H Suresh, P Szolovits, ... Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 40 | 2018 |
| The use of autoencoders for discovering patient phenotypes H Suresh, P Szolovits, M Ghassemi arXiv preprint arXiv:1703.07004, 2017 | 37 | 2017 |
| Intuitively assessing ML model reliability through example-based explanations and editing model inputs H Suresh, KM Lewis, J Guttag, A Satyanarayan Proceedings of the 27th International Conference on Intelligent User …, 2022 | 36 | 2022 |
| Tech worker organizing for power and accountability W Boag, H Suresh, B Lepe, C D'Ignazio Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 32 | 2022 |
| Understanding potential sources of harm throughout the machine learning life cycle H Suresh, J Guttag MIT Schwarzman College of Computing, 2021 | 32 | 2021 |
