Research

Research awards

Best Paper Runner-Up Award at WITS, 2021.
Google AI Award for Inclusion Research, 2020.
EECS Rising Stars, 2019.
Best Thematic Paper Award at NAACL, 2019.
Microsoft Research Dissertation Grant, 2018.
1st Place Innovation Award on Data Science at Data for Policy, 2016.
Best Student Presentation Award at Domestic Nuclear Detection Office (DNDO) Academic Research Initiative Grantees Conference, 2015.

Working papers

Leveraging Expert Consistency to Improve Algorithmic Decision Support (with V. Jeanselme, A. Dubrawski, A. Chouldechova)
Best Paper Runner-Up Award at WITS’21.

A case for humans-in-the-loop: decisions in the presence of misestimated algorithmic scores (with R. Fogliato, A. Chouldechova). Extended version of CHI’20.

Social Norm Bias: Residual Harms of Fairness-Aware Algorithms (with M. Cheng, L. Mackey, A. Kalai)
ICML ml4data Workshop Spotlight Talk, ICML Socially Responsible ML Workshop.

On Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making (with J. Schoeffer, N. Kuehl)

Conference Proceedings & Journals

Note on authorship order: As faculty, I tend to follow Business School convention, which does not give special meaning to the “last author” and orders according to contribution.

Google Scholar Profile

Toward Supporting Perceptual Complementarity in Human-AI Collaboration via Reflection on Unobservables. In Proceedings of the ACM on Human-Computer Interaction, (CSCW), 2023. With: Kenneth Holstein, Lakshmi Tumati, Yanghuidi Cheng.

Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. In Proceedings of the 2nd Machine Learning for Health symposium, PMLR, (ML4H) 2022. With: Vincent Jeanselme, Zhe Zhang, Jessica Barrett, Brian Tom.

Self-fulfilling prophecies and machine learning in resuscitation science. Resuscitation, 2022. With: Jonathan Elmer.

More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. In Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2022. With: Yunyi Li, Maytal Saar-Tsechansky.

Algorithmic Fairness in Business Analytics: Directions for Research and Practice. Production & Operations Management, 2022. With: Stefan Feuerriegel, Maytal Saar-Tsechansky.

Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2022. With: Terrence Neumann, Sina Fazelpour.

Diversity in Sociotechnical Machine Learning Systems. Big Data & Society, 2022. With: Sina Fazelpour.

Sex differences in post cardiac arrest discharge locations. Resuscitation Plus, 2021. With: Vincent Jeanselme, Jonathan Elmer, Sarah M. Perman, Artur Dubrawski.

Human-AI Collaboration with Bandit Feedback. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021. With: Ruijiang Gao, Maytal Saar-Tsechansky, Ligong Han, Min Kyung Lee, Matt Lease.

The effect of differential victim crime reporting on predictive policing systems. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2021. With: Nil-Jana Akpinar, Alexandra Chouldechova.

A case for humans-in-the-loop: decisions in the presence of erroneous algorithmic scores. In Proceedings of ACM CHI Conference on Human Factors in Computing Systems (CHI), 2020. With: Riccardo Fogliato, and Alexandra Chouldechova. [Video, Blog post summary]

What’s in a name? Reducing bias in bios without access to protected attributes. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019. With: Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Kalai. Best Thematic Paper Award.

Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. In Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT), 2019. With: Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Adam Kalai. [GitHub, Video]

What are the biases in my word embedding?. In Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2019. With: Nathaniel Swinger, Neil Heffernan IV, Mark Leiserson, Adam Kalai.

Predicting Neurological Recovery with Canonical Autocorrelation Embeddings. PLoS ONE, 2019. With: Jieshi Chen, Peter Huggins, Jonathan Elmer, Gilles Clermont, Artur Dubrawski. [GitHub]

Machine Learning for the Developing World. ACM Transactions on Management Information Systems, 2018. With: William Herlands, Daniel Neill, Artur Dubrawski.

Discovery of Complex Anomalous Patterns of Sexual Violence in El Salvador. Data for Policy, 2016. With: Artur Dubrawski. 1st Place Innovation Award on Data Science.

Analyzing Image Search Behaviour of Radiologists: Semantics and Prediction of Query Results. Journal of Digital Imaging, 2015. With: Ivan Eggel, Charles E. Kahn Jr., Henning Müller.

Comparing Image Search Behaviour in the ARRS GoldMiner Search Engine and a Clinical PACS/RIS. Journal of Biomedical Informatics, 2015. With: Ivan Eggel, Bao Do, Daniel Rubin, Charles E. Kahn Jr., Henning Müller.

Peer-Reviewed Workshops

Doubting AI Predictions: Influence-Driven Second Opinion Recommendation. ACM CHI Workshop on Trust and Reliance in AI-Human Teams (TRAIT), 2022. With: Alexandra Chouldechova, Artur Dubrawski.

On the Relationship Between Explanations, Fairness Perceptions, and Decisions
ACM CHI Workshop on Human-Centered Explainable AI (HCXAI), 2022. With: Jakob Schoeffer, Niklas Kuehl.

Social Norm Bias: Residual Harms of Fairness-Aware Algorithms. ICML ml4data Workshop Spotlight Talk & ICML Socially Responsible ML Workshop, 2021. With: Myra Cheng, Lester Mackey, Adam Kalai.

Lessons from the deployment of an algorithmic tool in child welfare. Fair & Responsible AI Workshop, CHI, 2020. With: Riccardo Fogliato, Alexandra Chouldechova.

Learning under selective labels in the presence of expert consistency. Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML), 2018. With: Artur Dubrawski, Alexandra Chouldechova.

Lass0: Sparse Non­Convex Regression by Local Search. NIPS Workshop on Optimization for Machine Learning, 2015. With: William Herlands, Daniel Neill, and Artur Dubrawski.