Suresh, Harini

Do as AI say: susceptibility in deployment of clinical decision-aids

Gaube, S., Suresh, H., Raue, M., Merritt, A., Berkowitz, S. J., Lermer, E., Coughlin, J. F., Guttag, J. V., Colak, E., & Ghassemi, M. (2021) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic Skills

Cited by 543

Underspecification Presents Challenges for Credibility in Modern Machine Learning

D'Amour, A., Heller, K., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., et al. (2020) · arXiv

Domain Adaptation and Few-Shot LearningAdversarial Robustness in Machine Learning

Cited by 398

Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs

Suresh, H., Gomez, S. R., Nam, K. K., & Satyanarayan, A. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 117

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays

Gaube, S., Suresh, H., Raue, M., Lermer, E., Koch, T. K., Hudecek, M. F. C., Ackery, A. D., Grover, S. C., Coughlin, J. F., Frey, D., et al. (2023) · Scientific Reports

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 92

A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle

Suresh, H., & Guttag, J. (2021) · Equity and Access in Algorithms, Mechanisms, and Optimization

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 82

A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle

Suresh, H., & Guttag, J. V. (2019) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 82

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 worsening health inequities

Kim, J. Y., Hasan, A., Kellogg, K. C., Ratliff, W., Murray, S. G., Suresh, H., Valladares, A., Shaw, K., Tobey, D., Vidal, D. E., et al. (2024) · PLOS Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 61

Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU

Suresh, H., Gong, J. J., & Guttag, J. V. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 54

Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

Suresh, H., Lao, N., & Liccardi, I. (2020) · 12th ACM Conference on Web Science

Human-Automation Interaction and SafetyExplainable Artificial Intelligence (XAI)

Cited by 49

Tech Worker Organizing for Power and Accountability

Boag, W., Suresh, H., Lepe, B., & D'Ignazio, C. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AILegal and Labor Studies

Cited by 34

Who should do as AI say? Only non-task expert physicians benefit from correct explainable AI advice

Gaube, S., Suresh, H., Raue, M., Lermer, E., Koch, T., Hudecek, M., Ackery, A. D., Grover, S. C., Coughlin, J. F., Frey, D., et al. (2022) · Research Square

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 3