Research

Manuscripts

• Lydia T. Liu^{^}, Inioluwa Deborah Raji^{^}, Angela Zhou^{^}, Luke Guerdan, Jessica Hullman, Daniel Malinsky, Bryan Wilder, Simone Zhang, Hammaad Adam, Amanda Coston, Ben Laufer, Ezinne Nwankwo, Michael Zanger-Tishler, Eli Ben-Michael, Solon Barocas, Avi Feller, Marissa Gerchick, Talia Gillis, Shion Guha, Daniel Ho, Lily Hu, Kosuke Imai, Sayash Kapoor, Joshua Loftus, Razieh Nabi, Arvind Narayanan, Ben Recht, Juan Carlos Perdomo, Matthew Salganik, Mark Sendak, Alexander Tolbert, Berk Ustun, Suresh Venkatasubramanian, Angelina Wang, Ashia Wilson.
  Bridging Prediction and Intervention Problems in Social Systems.
  Working White Paper, 2025. arxiv

  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 from the same population. In reality, these prediction systems operationalize holistic policy interventions in deployment. Once deployed, ADS can shape impacted population outcomes through an effective policy change in how decision-makers operate, while also being defined by past and present interactions between stakeholders and the limitations of existing organizational, as well as societal, infrastructure and context. In this work, we consider the ways in which we must shift from a prediction-focused paradigm to an intervention-oriented paradigm when considering the impact of ADS within social systems. We argue this requires a new default problem setup for ADS beyond prediction, to instead consider predictions as decision support, final decisions, and outcomes. We highlight how this perspective unifies modern statistical frameworks and other tools to study the design, implementation, and evaluation of ADS systems, and point to the research directions necessary to operationalize this paradigm shift. Using these tools, we characterize the limitations of focusing on isolated prediction tasks, and lay the foundation for a more intervention-oriented approach to developing and deploying ADS.

• Kara Schechtman, Benjamin Brandon, Jenise Stafford, Hannah Li^{^}, Lydia T. Liu^{^}.
  Discretion in the Loop: Human Expertise in Algorithm-Assisted College Advising.
  To appear (Non-archival), ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO). arxiv
  A preliminary version was presented at IC2S2, Norrköping, Sweden, July 2025, and at the Workshop on AI & Analytics for Social Good, College Park, MD, May 2025.

• Mark D. Verhagen, Benedikt Stroebl, Tiffany Liu, Lydia T. Liu, Matthew J. Salganik.
  The Book of Life approach: Enabling richness and scale for life course research.
  Working Paper, 2025. arxiv blogpost

• Romina Mahinpei, Victoria Dean, Ruth Fong, Lydia T. Liu, Manoel Horta Ribeiro.
  AI Assistance for Discretionary Work: Increasing Feedback Provision in Higher Education.
  In submission, 2026. arxiv

Peer-reviewed publications

Journal articles

• Joshua Cohen and Lydia T. Liu.
  The Reach of Fairness.
  ACM Journal on Responsible Computing. Just Accepted (February 2025). eprint preprint

• Lydia T. Liu^*, Serena Wang^*, Tolani Britton^{^}, Rediet Abebe^{^}.
  Reimagining the Machine Learning Life Cycle to Improve Educational Outcomes of Students.
  Proceedings of the National Academy of Sciences 120.9 (2023): e2204781120. eprint slides
  A preliminary version (“Promises and Pitfalls of Machine Learning in Education”) was presented as a poster at the Research Conference on Communications, Information, and Internet Policy (TPRC 2021).

• Lydia T. Liu, Feng Ruan, Horia Mania, Michael I. Jordan.
  Bandit Learning in Decentralized Matching Markets.
  Journal of Machine Learning Research, 22(211):1−34, 2021. journal arxiv
  Presented as a poster at Workshop on Operations of People-Centric Systems (EC ‘21)

• Zhizhen Zhao, Lydia T. Liu, Amit Singer.
  Steerable ePCA: Rotationally Invariant Exponential Family PCA.
  IEEE Transactions on Image Processing, vol. 29, pp. 6069-6081, 2020. doi arxiv

• Lydia T. Liu^*, Edgar Dobriban^*, and Amit Singer.
  ePCA: High Dimensional Exponential Family PCA.
  Annals of Applied Statistics 2018, Vol. 12, No. 4, 2121-2150. doi arxiv software

Conference proceedings

• Amaya Dharmasiri, William Yang, Polina Kirichenko, Lydia T. Liu, Olga Russakovsky.
  The Impact of Coreset Selection on Spurious Correlations and Group Robustness. poster
  Advances in Neural Information Processing Systems (NeurIPS), San Diego, CA, 2025.

• Inioluwa Deborah Raji and Lydia T. Liu.
  Evaluating Prediction-based Interventions with Human Decision Makers In Mind.
  Proceedings of The 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 2025. arxiv
  A preliminary version was presented as a poster at the ICML 2024 workshop on Humans, Algorithmic Decision-Making and Society.

• Lydia T. Liu, Solon Barocas, Jon Kleinberg, Karen Levy.
  On the Actionability of Outcome Prediction.
  Proceedings of the AAAI conference on Artificial Intelligence, 2024. doi arxiv

• Lydia T. Liu, Nikhil Garg, Christian Borgs.
  Strategic ranking.
  Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. arxiv
  Presented as a poster at the ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), 2021.

• Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock.
  Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning.
  Proceedings of the 37th International Conference on Machine Learning (ICML), 2020. arxiv

• Lydia T. Liu^*, Horia Mania^*, Michael I. Jordan.
  Competing Bandits in Matching Markets.
  Proceedings of The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. arxiv

• Lydia T. Liu, Ashia Wilson, Nika Haghtalab, Adam Tauman Kalai, Christian Borgs, Jennifer Chayes.
  The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally.
  Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (ACM FAT*), Barcelona, Spain, 2020. doi arxiv

• Lydia T. Liu^*, Max Simchowitz^*, Moritz Hardt.
  The Implicit Fairness Criterion of Unconstrained Learning.
  Proceedings of the 36th International Conference on Machine Learning (ICML), Long Beach, California, USA, 2019. arxiv code

• Chi Jin^*, Lydia T. Liu^*, Rong Ge, Michael I. Jordan.
  On the Local Minima of the Empirical Risk.
  Advances in Neural Information Processing Systems (NeurIPS) 32, Montréal, Canada, 2018. Spotlight. arxiv

• Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, Moritz Hardt.
  Delayed Impact of Fair Machine Learning.
  🏆 Proceedings of the 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, 2018. Best Paper Award. arxiv code

Workshop proceedings

• Jane Castleman, Nimra Nadeem, Tanvi Namjoshi, Lydia T. Liu.
  Rethinking Math Benchmarks: Implications for AI in Education.
  Proceedings of the Innovation and Responsibility in AI-Supported Education Workshop. paper

• Yuhan Liu, Yuhan Zheng, Siyuan Zhang, Lydia T. Liu.
  Evaluating Fairness in Black-box Algorithmic Markets: A Case Study of Ride Sharing in Chicago.
  International Conference on Machine Learning (ICML) Workshop on Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact. arxiv

• Benedikt Stroebl, Rajiv Krishna Swamy, Lydia T. Liu.
  A Baseline that Matters: Categorical Prioritization as Benchmark for Social Policy Algorithms.
  International Conference on Machine Learning (ICML) Workshop on Humans, Algorithmic Decision-Making and Society: Modeling Interactions and Impact. poster

• Esther Rolf, Max Simchowitz, Sarah Dean, Lydia T. Liu, Daniel Björkegren, Moritz Hardt, Joshua Blumenstock.
  Balancing Competing Objectives for Welfare-Aware Machine Learning with Imperfect Data.
  🏆 NeurIPS Joint Workshop on AI for Social Good, Vancouver, Canada, 2019. Best Paper Award.

• Lydia T. Liu, Urun Dogan, and Katja Hofmann.
  Decoding multitask DQN in the world of Minecraft.
  The 13th European Workshop on Reinforcement Learning, Barcelona, Spain, 2016. Also presented at the 11th Women in Machine Learning Workshop and the Deep Reinforcement Learning Workshop at NeurIPS 2016.

^{* ^} equal contribution