Adnostic: Privacy Preserving Targeted Advertising. - Vincent Toubiana; Arvind Narayanan; Dan Boneh; Helen Nissenbaum; Solon Barocas (NDSS, 2010)

The price of precision: voter microtargeting and its potential harms to the democratic process. - Solon Barocas (PLEAD@CIKM, 2012)

A Critical Look at Decentralized Personal Data Architectures - Arvind Narayanan; Vincent Toubiana; Solon Barocas; Helen Nissenbaum; Dan Boneh (CoRR, 2012)

Big data's end run around procedural privacy protections. - Solon Barocas; Helen Nissenbaum (Commun. ACM, 2014)

WSDM 2016 Workshop on the Ethics of Online Experimentation. - Fernando Diaz 001; Solon Barocas (WSDM, 2016)

Social and Technical Trade-Offs in Data Science. - Solon Barocas; danah boyd; Sorelle A. Friedler; Hanna M. Wallach (Big Data, 2017)

Engaging the ethics of data science in practice. - Solon Barocas; danah boyd (Commun. ACM, 2017)

Big Data, Data Science, and Civil Rights. - Solon Barocas; Elizabeth Bradley; Vasant G. Honavar; Foster J. Provost (CoRR, 2017)

Ten simple rules for responsible big data research. - Matthew Zook; Solon Barocas; danah boyd; Kate Crawford 002; Emily Keller; Seeta Peña Gangadharan; Alyssa Goodman; Rachelle Hollander; Barbara König 002; Jacob Metcalf; Arvind Narayanan; Alondra Nelson; et al. (PLoS Comput. Biol., 2017)

Debiasing Desire: Addressing Bias & Discrimination on Intimate Platforms. - Jevan A. Hutson; Jessie G. Taft; Solon Barocas; Karen Levy (Proc. ACM Hum. Comput. Interact., 2018)

Problem Formulation and Fairness. - Samir Passi; Solon Barocas (FAT, 2019)

Mitigating Bias in Algorithmic Employment Screening: Evaluating Claims and Practices. - Manish Raghavan; Solon Barocas; Jon M. Kleinberg; Karen Levy (CoRR, 2019)

Language (Technology) is Power: A Critical Survey of "Bias" in NLP. - Su Lin Blodgett; Solon Barocas; Hal Daumé III; Hanna M. Wallach (ACL, 2020)

Roles for computing in social change. - Rediet Abebe; Solon Barocas; Jon M. Kleinberg; Karen Levy; Manish Raghavan; David G. Robinson (FAT*, 2020)

When not to design, build, or deploy. - Solon Barocas; Asia J. Biega; Benjamin Fish; Jedrzej Niklas; Luke Stark (FAT*, 2020)

The hidden assumptions behind counterfactual explanations and principal reasons. - Solon Barocas; Andrew D. Selbst; Manish Raghavan (FAT*, 2020)

The meaning and measurement of bias: lessons from natural language processing. - Abigail Z. Jacobs; Su Lin Blodgett; Solon Barocas; Hal Daumé III; Hanna M. Wallach (FAT*, 2020)

Mitigating bias in algorithmic hiring: evaluating claims and practices. - Manish Raghavan; Solon Barocas; Jon M. Kleinberg; Karen Levy (FAT*, 2020)

Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs. - Solon Barocas; Anhong Guo; Ece Kamar; Jacquelyn Krones; Meredith Ringel Morris; Jennifer Wortman Vaughan; W. Duncan Wadsworth; Hanna M. Wallach (AIES, 2021)

Computer Vision and Conflicting Values: Describing People with Automated Alt Text. - Margot J. Hanley; Solon Barocas; Karen Levy; Shiri Azenkot; Helen Nissenbaum (AIES, 2021)

Algorithmic Auditing and Social Justice: Lessons from the History of Audit Studies. - Briana Vecchione; Karen Levy; Solon Barocas (EAAMO, 2021)

Better Together?: How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness. - Kate Donahue; Solon Barocas (FAccT, 2021)

Responsible computing during COVID-19 and beyond. - Solon Barocas; Asia J. Biega; Margarita Boyarskaya; Kate Crawford 002; Hal Daumé III; Miroslav Dudík; Benjamin Fish; Mary L. Gray; Brent J. Hecht; Alexandra Olteanu; Forough Poursabzi-Sangdeh; Luke Stark; et al. (Commun. ACM, 2021)

Mimetic Models: Ethical Implications of AI that Acts Like You. - Reid McIlroy-Young; Jon M. Kleinberg; Siddhartha Sen 001; Solon Barocas; Ashton Anderson (AIES, 2022)

Disentangling the Components of Ethical Research in Machine Learning. - Carolyn Ashurst; Solon Barocas; Rosie Campbell; Deborah Raji (FAccT, 2022)

Model Multiplicity: Opportunities, Concerns, and Solutions. - Emily Black; Manish Raghavan; Solon Barocas (FAccT, 2022)

REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research. - Jessie J. Smith; Saleema Amershi; Solon Barocas; Hanna M. Wallach; Jennifer Wortman Vaughan (FAccT, 2022)

Measuring Representational Harms in Image Captioning. - Angelina Wang; Solon Barocas; Kristen Laird; Hanna M. Wallach (FAccT, 2022)

An Uncommon Task: Participatory Design in Legal AI. - Fernando A. Delgado; Solon Barocas; Karen Levy (Proc. ACM Hum. Comput. Interact., 2022)

On modeling human perceptions of allocation policies with uncertain outcomes. - Hoda Heidari; Solon Barocas; Jon M. Kleinberg; Karen Levy (SIGecom Exch., 2022)

Taxonomizing and Measuring Representational Harms: A Look at Image Tagging. - Jared Katzman; Angelina Wang; Morgan Klaus Scheuerman; Su Lin Blodgett; Kristen Laird; Hanna M. Wallach; Solon Barocas (AAAI, 2023)

Informational Diversity and Affinity Bias in Team Growth Dynamics. - Hoda Heidari; Solon Barocas; Jon M. Kleinberg; Karen Levy (EAAMO, 2023)

Against Predictive Optimization: On the Legitimacy of Decision-Making Algorithms that Optimize Predictive Accuracy. - Angelina Wang; Sayash Kapoor; Solon Barocas; Arvind Narayanan (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)

Variance, Self-Consistency, and Arbitrariness in Fair Classification. - A. Feder Cooper; Solon Barocas; Christopher De Sa; Siddhartha Sen 001 (CoRR, 2023)

Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification. - A. Feder Cooper; Katherine Lee; Madiha Zahrah Choksi; Solon Barocas; Christopher De Sa; James Grimmelmann; Jon M. Kleinberg; Siddhartha Sen 001; Baobao Zhang (AAAI, 2024)

On the Actionability of Outcome Prediction. - Lydia T. Liu; Solon Barocas; Jon M. Kleinberg; Karen Levy (AAAI, 2024)

Measuring machine learning harms from stereotypes: requires understanding who is being harmed by which errors in what ways. - Angelina Wang; Xuechunzi Bai; Solon Barocas; Su Lin Blodgett (CoRR, 2024)

The Legal Duty to Search for Less Discriminatory Algorithms. - Emily Black; Logan Koepke; Pauline Kim; Solon Barocas; Mingwei Hsu (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)

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)

Fundamental Limits in the Search for Less Discriminatory Algorithms - and How to Avoid Them. - Benjamin Laufer; Manisch Raghavan; Solon Barocas (CoRR, 2024)

Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review. - Rock Yuren Pang; Hope Schroeder; Kynnedy Simone Smith; Solon Barocas; Ziang Xiao; Emily Tseng; Danielle Bragg (CHI, 2025)

What Constitutes a Less Discriminatory Algorithm? - Benjamin Laufer; Manish Raghavan; Solon Barocas (CSLAW, 2025)

Disclosure without Engagement: An Empirical Review of Positionality Statements at FAccT. - Hope Schroeder; Akshansh Pareek; Solon Barocas (FAccT, 2025)

Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Ways. - Angelina Wang; Xuechunzi Bai; Solon Barocas; Su Lin Blodgett (FAccT, 2025)

Designing Algorithmic Delegates: the Role of Indistinguishability in Human-AI Handoff. - Sophie Greenwood; Karen Levy; Solon Barocas; Hoda Heidari; Jon M. Kleinberg (EC, 2025)

Supporting Industry Computing Researchers in Assessing, Articulating, and Addressing the Potential Negative Societal Impact of Their Work. - Wesley Hanwen Deng; Solon Barocas; Jennifer Wortman Vaughan (Proc. ACM Hum. Comput. Interact., 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)

AI Automatons: AI Systems Intended to Imitate Humans. - Alexandra Olteanu; Solon Barocas; Su Lin Blodgett; Lisa Egede; Alicia DeVrio; Myra Cheng (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)

Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor. - Alexandra Olteanu; Su Lin Blodgett; Agathe Balayn; Angelina Wang; Fernando Diaz 001; Flávio du Pin Calmon; Margaret Mitchell; Michael D. Ekstrand; Reuben Binns; Solon Barocas (CoRR, 2025)

Distinguishing Predictive and Generative AI in Regulation. - Jennifer Wang; Andrew D. Selbst; Solon Barocas; Suresh Venkatasubramanian (CoRR, 2025)

Bridging Prediction and Intervention Problems in Social Systems. - Lydia T. Liu; Inioluwa Deborah Raji; Angela Zhou; Luke Guerdan; Jessica Hullman; Daniel Malinsky; Bryan Wilder; Simone Zhang; Hammaad Adam; Amanda Coston; Benjamin Laufer; Ezinne Nwankwo; et al. (CoRR, 2025)