activity
20162026
most citedEvaluating Fairness in Black-box Algorithmic Markets: A Case Study of Ride Sharing in Chicago

1 citations · 2 across the 11 of their papers we have counts for

collaborators

20 papers

cs.LG2026

Births are difficult to predict even with rich survey and full-population register data

Elizaveta Sivak, Emily M. Cantrell, Thomas Emery +109

Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child w…

cs.CY2026

Bridging Predictions and Interventions: An Integrated Framework for Automated Decision-Systems

Inioluwa Deborah Raji, Lydia T. Liu, Angela Zhou +27

Automated decision systems (ADS) leverage predictions about individual future outcomes to inform consequential decision-making in organizational settings. Across various settings -…

cs.HC2026

AI Assistance for Discretionary Work: Increasing Feedback Provision in Higher Education

Romina Mahinpei, Victoria Dean, Ruth Fong +2

AI systems increasingly shape human workflows by generating intermediate artifacts that users can adopt, revise, or ignore. While prior work has shown that AI assistance can improv…

cs.CL2025

The Book of Life approach: Enabling richness and scale for life course research

Mark D. Verhagen, Benedikt Stroebl, Tiffany Liu +2

For over a century, life course researchers have faced a choice between two dominant methodological approaches: qualitative methods that analyze rich data but are constrained to sm…

cs.LG2025

The Impact of Coreset Selection on Spurious Correlations and Group Robustness

Amaya Dharmasiri, William Yang, Polina Kirichenko +2

Coreset selection methods have shown promise in reducing the training data size while maintaining model performance for data-efficient machine learning. However, as many datasets s…

cs.LG2025

Bridging Prediction and Intervention Problems in Social Systems

Lydia T. Liu, Inioluwa Deborah Raji, Angela Zhou +32

Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals…