Differences in search engine evaluations between query owners and non-owners. - Alexandra Chouldechova; David Mease (WSDM, 2013)

Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. - Alexandra Chouldechova (Big Data, 2017)

Fairer and more accurate, but for whom? - Alexandra Chouldechova; Max G'Sell (CoRR, 2017)

Does mitigating ML's disparate impact require disparate treatment? - Zachary C. Lipton; Alexandra Chouldechova; Julian J. McAuley (CoRR, 2017)

A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. - Alexandra Chouldechova; Diana Benavides Prado; Oleksandr Fialko; Rhema Vaithianathan (FAT, 2018)

Does mitigating ML's impact disparity require treatment disparity? - Zachary C. Lipton; Julian J. McAuley; Alexandra Chouldechova (NeurIPS, 2018)

Learning under selective labels in the presence of expert consistency. - Maria De-Arteaga; Artur Dubrawski; Alexandra Chouldechova (CoRR, 2018)

The Frontiers of Fairness in Machine Learning. - Alexandra Chouldechova; Aaron Roth 001 (CoRR, 2018)

Toward Algorithmic Accountability in Public Services: A Qualitative Study of Affected Community Perspectives on Algorithmic Decision-making in Child Welfare Services. - Anna Brown; Alexandra Chouldechova; Emily Putnam-Hornstein; Andrew Tobin; Rhema Vaithianathan (CHI, 2019)

Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting. - Maria De-Arteaga; Alexey Romanov; Hanna M. Wallach; Jennifer T. Chayes; Christian Borgs; Alexandra Chouldechova; Sahin Cem Geyik; Krishnaram Kenthapadi; Adam Tauman Kalai (FAT, 2019)

What's in a Name? Reducing Bias in Bios without Access to Protected Attributes. - Alexey Romanov; Maria De-Arteaga; Hanna M. Wallach; Jennifer T. Chayes; Christian Borgs; Alexandra Chouldechova; Sahin Cem Geyik; Krishnaram Kenthapadi; Anna Rumshisky; Adam Kalai (NAACL-HLT (1), 2019)

Counterfactual Risk Assessments, Evaluation, and Fairness. - Amanda Coston; Alexandra Chouldechova; Edward H. Kennedy (CoRR, 2019)

Fairness Evaluation in Presence of Biased Noisy Labels. - Riccardo Fogliato; Alexandra Chouldechova; Max G'Sell (AISTATS, 2020)

A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores. - Maria De-Arteaga; Riccardo Fogliato; Alexandra Chouldechova (CHI, 2020)

Counterfactual risk assessments, evaluation, and fairness. - Amanda Coston; Alan Mishler; Edward H. Kennedy; Alexandra Chouldechova (FAT*, 2020)

Counterfactual Predictions under Runtime Confounding. - Amanda Coston; Edward H. Kennedy; Alexandra Chouldechova (NeurIPS, 2020)

A snapshot of the frontiers of fairness in machine learning. - Alexandra Chouldechova; Aaron Roth 001 (Commun. ACM, 2020)

On the Validity of Arrest as a Proxy for Offense: Race and the Likelihood of Arrest for Violent Crimes. - Riccardo Fogliato; Alice Xiang; Zachary C. Lipton; Daniel Nagin; Alexandra Chouldechova (AIES, 2021)

Soliciting Stakeholders' Fairness Notions in Child Maltreatment Predictive Systems. - Hao Fei Cheng; Logan Stapleton; Ruiqi Wang; Paige Bullock; Alexandra Chouldechova; Zhiwei Steven Wu; Haiyi Zhu (CHI, 2021)

The effect of differential victim crime reporting on predictive policing systems. - Nil-Jana Akpinar; Maria De-Arteaga; Alexandra Chouldechova (FAccT, 2021)

Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy. - Amanda Coston; Neel Guha; Derek Ouyang; Lisa Lu; Alexandra Chouldechova; Daniel E. Ho (FAccT, 2021)

Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds. - Alan Mishler; Edward H. Kennedy; Alexandra Chouldechova (FAccT, 2021)

Characterizing Fairness Over the Set of Good Models Under Selective Labels. - Amanda Coston; Ashesh Rambachan; Alexandra Chouldechova (ICML, 2021)

Using a Machine Learning Tool to Support High-Stakes Decisions in Child Protection. - Rhema Vaithianathan; Diana Benavides Prado; Eric E. Dalton; Alexandra Chouldechova; Emily Putnam-Hornstein (AI Mag., 2021)

Leveraging Expert Consistency to Improve Algorithmic Decision Support. - Maria De-Arteaga; Artur Dubrawski; Alexandra Chouldechova (CoRR, 2021)

The Impact of Algorithmic Risk Assessments on Human Predictions and its Analysis via Crowdsourcing Studies. - Riccardo Fogliato; Alexandra Chouldechova; Zachary C. Lipton (Proc. ACM Hum. Comput. Interact., 2021)

Unsupervised and Semi-supervised Bias Benchmarking in Face Recognition. - Alexandra Chouldechova; Siqi Deng; Yongxin Wang; Wei Xia; Pietro Perona (ECCV (13), 2022)

Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models. - Emily Black; Hadi Elzayn; Alexandra Chouldechova; Jacob Goldin; Daniel E. Ho (FAccT, 2022)

Human-Algorithm Collaboration: Achieving Complementarity and Avoiding Unfairness. - Kate Donahue; Alexandra Chouldechova; Krishnaram Kenthapadi (FAccT, 2022)

Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. - Logan Stapleton; Min Hun Lee; Diana Qing; Marya Wright; Alexandra Chouldechova; Ken Holstein; Zhiwei Steven Wu; Haiyi Zhu (FAccT, 2022)

Doubting AI Predictions: Influence-Driven Second Opinion Recommendation. - Maria De-Arteaga; Alexandra Chouldechova; Artur Dubrawski (CoRR, 2022)

Heterogeneity in Algorithm-Assisted Decision-Making: A Case Study in Child Abuse Hotline Screening. - Lingwei Cheng; Alexandra Chouldechova (Proc. ACM Hum. Comput. Interact., 2022)

Overcoming Algorithm Aversion: A Comparison between Process and Outcome Control. - Lingwei Cheng; Alexandra Chouldechova (CHI, 2023)

Examining risks of racial biases in NLP tools for child protective services. - Anjalie Field; Amanda Coston; Nupoor Gandhi; Alexandra Chouldechova; Emily Putnam-Hornstein; David Steier; Yulia Tsvetkov (FAccT, 2023)

Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints. - Jamelle Watson-Daniels; Solon Barocas; Jake M. Hofman; Alexandra Chouldechova (FAccT, 2023)

Algorithm-Assisted Decision Making and Racial Disparities in Housing: A Study of the Allegheny Housing Assessment Tool. - Lingwei Cheng; Cameron Drayton; Alexandra Chouldechova; Rhema Vaithianathan (AIES (1), 2024)

Do Responsible AI Artifacts Advance Stakeholder Goals? Four Key Barriers Perceived by Legal and Civil Stakeholders. - Anna Kawakami; Daricia Wilkinson; Alexandra Chouldechova (AIES (1), 2024)

The Impact of Differential Feature Under-reporting on Algorithmic Fairness. - Nil-Jana Akpinar; Zachary C. Lipton; Alexandra Chouldechova (FAccT, 2024)

A structured regression approach for evaluating model performance across intersectional subgroups. - Christine Herlihy; Kimberly Truong; Alexandra Chouldechova; Miroslav Dudík (FAccT, 2024)

SureMap: Simultaneous mean estimation for single-task and multi-task disaggregated evaluation. - Misha Khodak; Lester Mackey; Alexandra Chouldechova; Miro Dudík (NeurIPS, 2024)

SureMap: Simultaneous Mean Estimation for Single-Task and Multi-Task Disaggregated Evaluation. - Mikhail Khodak; Lester Mackey; Alexandra Chouldechova; Miroslav Dudík (CoRR, 2024)

Evaluating Generative AI Systems is a Social Science Measurement Challenge. - Hanna M. Wallach; Meera A. Desai; Nicholas Pangakis; A. Feder Cooper; Angelina Wang; Solon Barocas; Alexandra Chouldechova; Chad Atalla; Su Lin Blodgett; Emily Corvi; P. Alex Dow; Jean Garcia-Gathright; et al. (CoRR, 2024)

Dimensions of Generative AI Evaluation Design. - P. Alex Dow; Jennifer Wortman Vaughan; Solon Barocas; Chad Atalla; Alexandra Chouldechova; Hanna M. Wallach (CoRR, 2024)

A Framework for Evaluating LLMs Under Task Indeterminacy. - Luke Guerdan; Hanna M. Wallach; Solon Barocas; Alexandra Chouldechova (CoRR, 2024)

Gaps Between Research and Practice When Measuring Representational Harms Caused by LLM-Based Systems. - Emma Harvey; Emily Sheng; Su Lin Blodgett; Alexandra Chouldechova; Jean Garcia-Gathright; Alexandra Olteanu; Hanna M. Wallach (CoRR, 2024)

A Shared Standard for Valid Measurement of Generative AI Systems' Capabilities, Risks, and Impacts. - Alexandra Chouldechova; Chad Atalla; Solon Barocas; A. Feder Cooper; Emily Corvi; P. Alex Dow; Jean Garcia-Gathright; Nicholas Pangakis; Stefanie Reed; Emily Sheng; Dan Vann; Matthew Vogel; et al. (CoRR, 2024)

Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice. - A. Feder Cooper; Christopher A. Choquette-Choo; Miranda Bogen; Matthew Jagielski; Katja Filippova; Ken Ziyu Liu; Alexandra Chouldechova; Jamie Hayes; Yangsibo Huang; Niloofar Mireshghallah; Ilia Shumailov; Eleni Triantafillou; et al. (CoRR, 2024)

Taxonomizing Representational Harms using Speech Act Theory. - Emily Corvi; Hannah Washington; Stefanie Reed; Chad Atalla; Alexandra Chouldechova; P. Alex Dow; Jean Garcia-Gathright; Nicholas J. Pangakis; Emily Sheng; Dan Vann; Matthew Vogel; Hanna M. Wallach (ACL (Findings), 2025)

Understanding and Meeting Practitioner Needs When Measuring Representational Harms Caused by LLM-Based Systems. - Emma Harvey; Emily Sheng; Su Lin Blodgett; Alexandra Chouldechova; Jean Garcia-Gathright; Alexandra Olteanu; Hanna M. Wallach (ACL (Findings), 2025)

Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge. - Hanna M. Wallach; Meera A. Desai; A. Feder Cooper; Angelina Wang; Chad Atalla; Solon Barocas; Su Lin Blodgett; Alexandra Chouldechova; Emily Corvi; P. Alex Dow; Jean Garcia-Gathright; Alexandra Olteanu; et al. (CoRR, 2025)

Validating LLM-as-a-Judge Systems in the Absence of Gold Labels. - Luke Guerdan; Solon Barocas; Kenneth Holstein; Hanna M. Wallach; Zhiwei Steven Wu; Alexandra Chouldechova (CoRR, 2025)