32 citations · 91 across the 16 of their papers we have counts for
20 papers
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…
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 -…
AI Assistance for Discretionary Work: Increasing Feedback Provision in Higher Education
Romina Mahinpei, Victoria Dean, Ruth Fong +2
AI assistance can increase the efficiency and accuracy of human work, but can it also encourage people to undertake valuable tasks they might otherwise skip? Here, we investigate w…
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…
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…
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…
Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising
Kara Schechtman, Benjamin Brandon, Jenise Stafford +2
In higher education, many institutions use algorithmic alerts to flag at-risk students and deliver advising at scale. While much research has focused on evaluating algorithmic pred…
Evaluating Prediction-based Interventions with Human Decision Makers In Mind
Inioluwa Deborah Raji, Lydia Liu
Automated decision systems (ADS) are broadly deployed to inform and support human decision-making across a wide range of consequential settings. However, various context-specific d…
Evaluating Fairness in Black-box Algorithmic Markets: A Case Study of Ride Sharing in Chicago
Yuhan Liu, Yuhan Zheng, Siyuan Zhang +1
This study examines fairness within the rideshare industry, focusing on both drivers' wages and riders' trip fares. Through quantitative analysis, we found that drivers' hourly wag…
On the Actionability of Outcome Prediction
Lydia T. Liu, Solon Barocas, Jon Kleinberg +1
Predicting future outcomes is a prevalent application of machine learning in social impact domains. Examples range from predicting student success in education to predicting diseas…
Lost in Translation: Reimagining the Machine Learning Life Cycle in Education
Lydia T. Liu, Serena Wang, Tolani Britton +1
Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the…
Strategic Ranking
Lydia T. Liu, Nikhil Garg, Christian Borgs
Strategic classification studies the design of a classifier robust to the manipulation of input by strategic individuals. However, the existing literature does not consider the eff…
Bandit Learning in Decentralized Matching Markets
Lydia T. Liu, Feng Ruan, Horia Mania +1
We study two-sided matching markets in which one side of the market (the players) does not have a priori knowledge about its preferences for the other side (the arms) and is requir…
Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
Esther Rolf, Max Simchowitz, Sarah Dean +4
While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic polici…
The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally
Lydia T. Liu, Ashia Wilson, Nika Haghtalab +3
The long-term impact of algorithmic decision making is shaped by the dynamics between the deployed decision rule and individuals' response. Focusing on settings where each individu…
Competing Bandits in Matching Markets
Lydia T. Liu, Horia Mania, Michael I. Jordan
Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned. With the advent of massive on…
The implicit fairness criterion of unconstrained learning
Lydia T. Liu, Max Simchowitz, Moritz Hardt
We clarify what fairness guarantees we can and cannot expect to follow from unconstrained machine learning. Specifically, we characterize when unconstrained learning on its own imp…
Delayed Impact of Fair Machine Learning
Lydia T. Liu, Sarah Dean, Esther Rolf +2
Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventi…
On the Local Minima of the Empirical Risk
Chi Jin, Lydia T. Liu, Rong Ge +1
Population risk is always of primary interest in machine learning; however, learning algorithms only have access to the empirical risk. Even for applications with nonconvex nonsmoo…
PCA: High Dimensional Exponential Family PCA
Lydia T. Liu, Edgar Dobriban, Amit Singer
Many applications, such as photon-limited imaging and genomics, involve large datasets with noisy entries from exponential family distributions. It is of interest to estimate the c…