“Helping Me Versus Doing It for Me”: Designing for Agency in LLM-Infused Writing Tools for Science Journalism
Sachita Nishal, Mina Lee, Nicholas Diakopoulos, Jennifer Wortman Vaughan
CHI Conference on Human Factors in Computing Systems | April 2026
Jenn Wortman Vaughan
Senior Principal Research Manager
출판물
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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
ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2025) | November 2025
Honorable Mention
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Position: To Make Text-to-Image Models that Work for Marginalized Communities, We Need New Measurement Practices for the Long Tail
Nari Johnson, Hamna ., Deepthi Sudharsan, Theo Holroyd, Samantha Dalal, Siobhan Mackenzie Hall, Jennifer Wortman Vaughan, Daniela Massiceti, Cecily Morrison
July 2025
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Examining the Expanding Role of Synthetic Data Throughout the AI Development Pipeline
Shivani Kapania, Stephanie Ballard, Alex Kessler, Jennifer Wortman Vaughan
8th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2025) | June 2025
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Canvil: Designerly Adaptation for LLM-Powered User Experiences
K. Feng, Q. Vera Liao, Ziang Xiao, Jennifer Wortman Vaughan, Amy Zhang, David W. McDonald
CHI 2025 | April 2025
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Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao, Tania Lombrozo, Olga Russakovsky
CHI 2025 | April 2025
Honorable Mention, CHI 2025
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Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, Jennifer Wortman Vaughan
ToCHI | April 2025, 제 32 권(1)
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Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, A. Feder Cooper, Angelina Wang, Chad Atalla, Solon Barocas, Su Lin Blodgett, Alex Chouldechova, Emily Corvi, Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Nick Pangakis, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs
ICML 2025 | January 2025
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Machine Unlearning Doesn’t Do What You Think: Lessons for Generative AI Policy, Research, and Practice
A. F. Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu Liu, Alex Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, N. Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, A. Cyphert, Mark A. Lemley, danah boyd, Jennifer Wortman Vaughan, M. Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna Wallach, Nicolas Papernot, Katherine Lee
NeurIPS 2025 | December 2024
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Challenges in Human-Agent Communication
Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi, Eric Horvitz, Adam Fourney, Hussein Mozannar, Victor Dibia, Daniel S. Weld
MSR-TR-2024-53 | December 2024
글쓴이 Microsoft
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Microsoft New Future of Work Report 2024
Jenna Butler, Mihaela Vorvoreanu, Rebecca Janssen, Abigail Sellen, Nicole Immorlica, Adam Troy, Advait Sarkar, Alex Farach, Alex Chouldechova, Alexandra Olteanu, Alexia Cambon, Arjun Radhakrishna, Asta Roseway, Ben Zorn, Brent Hecht, Daniel G. Goldstein, Dhruv Joshi, Ed Cutrell, Emre Kiciman, Gonzalo Ramos, Gustavo Soares, Hanna Wallach, Ian Drosos, Jack Williams (johnwilliams), Jacki O'Neill, Jake Hofman, Jaime Teevan, Javier Hernandez, Jennifer Wortman Vaughan, Jina Suh, John Tang, Justin Edwards, Kalika Bali, Kori Inkpen, Krishna Madhavan, Laylah Bulman, Leon Reicherts, Lev Tankelevitch, Longqi Yang, Martez Mott, Millicent Ochieng, Mercy Muchai, Nancy Baym, Najeeb Abdulhamid, Nicolai Marquardt, Ken Hinckley, Michael Bentley, Dave Brown, Hugo Romat, Nathalie Henry Riche, Samuel Maina, Shamsi Iqbal, Siân Lindley, Stephanie Nyairo, Su Lin Blodgett, Sumit Gulwani, Sunayana Sitaram, Vu Le
MSR-TR-2024-56 | December 2024
글쓴이 Microsoft
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Dimensions of Generative AI Evaluation Design
Alex Dow, Jennifer Wortman Vaughan, Solon Barocas, Chad Atalla, Alex Chouldechova, Hanna Wallach
November 2024
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Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, Nick Pangakis, A. Feder Cooper, Angelina Wang, Solon Barocas, Alex Chouldechova, Chad Atalla, Su Lin Blodgett, Emily Corvi, Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs
November 2024
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(De)Noise: Moderating the Inconsistency Between Human Decision-Makers
Nina Grgić-Hlača, Junaid Ali, Krishna P. Gummadi, Jennifer Wortman Vaughan
ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2024) | November 2024
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“I’m Not Sure, But…”: Examining the Impact of Large Language Models’ Uncertainty Expression on User Reliance and Trust
Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, Jennifer Wortman Vaughan
7th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2024) | June 2024
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The CLeAR Documentation Framework for AI Transparency: Recommendations for Practitioners & Context for Policymakers
Kasia Chmielinski, Sarah Newman, Chris N. Kranzinger, Michael Hind, Jennifer Wortman Vaughan, Margaret Mitchell, Julia Stoyanovich, Angelina McMillan-Major, Emily McReynolds, Kathleen Esfahany, Mary L. Gray, Audrey Chang, Maui Hudson
April 2024
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Tinker, Tailor, Configure, Customize: The Articulation Work of Customizing AI Fairness Checklists
Michael Madaio, Jingya Chen, Hanna Wallach, Jennifer Wortman Vaughan
Computer-Supported Cooperative Work | April 2024
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AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
Q. Vera Liao, Jennifer Wortman Vaughan
Harvard Data Science Review | February 2024
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Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
Proc. ACM Hum.-Comput. Interact. | October 2023, 제 7 권
Article 370
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Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | April 2023
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GAM Coach: Towards Interactive and User-Centered Algorithmic Recourse
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | February 2023
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Understanding Machine Learning Practitioners’ Data Documentation Perceptions, Needs, Challenges, and Desiderata
Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, Jennifer Wortman Vaughan
2022 Conference on Computer Supported Cooperative Work | November 2022
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Generation Probabilities are Not Enough: Improving Error Highlighting for AI Code Suggestions
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, Jennifer Wortman Vaughan
NeurIPS Workshop on Human-Centered AI | October 2022
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Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
KDD 2022 | July 2022
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REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, Jennifer Wortman Vaughan
FAccT 2022 | May 2022
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Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support
Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, Hanna Wallach
25th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2022) | February 2022
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A Human-Centered Agenda for Intelligible Machine Learning
Jennifer Wortman Vaughan, Hanna Wallach
In Machines We Trust: Perspectives on Dependable AI | 글쓴이 MIT Press | 2021
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GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2021 Neural Information Processing Systems | December 2021
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Datasheets for Datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford
Communications of the ACM | December 2021, 제 64 권(12): pp. 86-92
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Summarize with Caution: Comparing Global Feature Attributions
Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna Wallach, Jennifer Wortman Vaughan
Bulletin of the IEEE Computer Society Technical Committee on Data Engineering | December 2021
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From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna Wallach, Jennifer Wortman Vaughan
HCOMP 2021 | September 2021
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Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, 제 67 권(7): pp. 30-32
https://doi.org/10.1145/3466612
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Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2021 | May 2021
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Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, Duncan Wadsworth, Hanna Wallach
AIES 2021 | May 2021
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No-Regret and Incentive-Compatible Online Learning
Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan
ICML 2020 | July 2020
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A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition
Forough Poursabzi-Sangdeh, Samira Samadi, Jennifer Wortman Vaughan, Hanna Wallach
April 2020
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
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Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI
Michael Madaio, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach
CHI Conference on Human Factors in Computing Systems | March 2020
CHI 2020 Best Paper Award
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Weight of Evidence as a Basis for Human-Oriented Explanations
David Alvarez-Melis, Hal Daumé III, Jennifer Wortman Vaughan, Hanna Wallach
Workshop on Human-Centric Machine Learning at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. | October 2019
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Toward Fairness in AI for People with Disabilities: A Research Roadmap
Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna Wallach, Meredith Ringel Morris
ASSETS 2019 Workshop on AI Fairness for People with Disabilities | October 2019
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Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
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Truthful Aggregation of Budget Proposals
Rupert Freeman, David Pennock, Dominik Peters, Jennifer Wortman Vaughan
Twentieth ACM Conference on Economics and Computation | June 2019
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Using Search Queries to Understand Health Information Needs in Africa
Rediet Abebe, Shawndra Hill, Jennifer Wortman Vaughan, Peter M. Small, H. Andrew Schwartz
Proceedings of the International AAAI Conference on Web and Social Media | June 2019
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Yin, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2019 | May 2019
Honorable Mention Award at CHI 2019
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
Honorable Mention Award at CHI 2019
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The Disparate Effects of Strategic Manipulation
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
2nd ACM Conference on Fairness, Accountability, and Transparency | January 2019
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An Equivalence Between Wagering and Fair-Division Mechanisms
Rupert Freeman, David Pennock, Jennifer Wortman Vaughan
AAAI 2019 | January 2019
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Group Fairness for Indivisible Goods Allocation
Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan
AAAI 2019 | January 2019
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Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets
Hoda Heidari, Sebastien Lahaie, David Pennock, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation (TEAC) - Special Issue on EC'15 | November 2018, 제 6 권(3–4)
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Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, 제 18 권(193): pp. 1-46
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Incentive-Compatible Forecasting Competitions
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David Pennock, Andreas Krause
Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018) | April 2018
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The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
31st Annual Conference on Learning Theory | February 2018
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Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
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The Double Clinching Auction for Wagering
Rupert Freeman, David Pennock, Jennifer Wortman Vaughan
EC'17, Cambridge, Massachusetts, USA | June 2017
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A Decomposition of Forecast Error in Prediction Markets
Miro Dudík, Sébastien Lahaie, Ryan Rogers, Jennifer Wortman Vaughan
31st Conference on Neural Information Processing Systems (NIPS 2017) | February 2017
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Mathematical Foundations for Social Computing
Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, Jennifer Wortman Vaughan
Communications of the ACM | December 2016, 제 59 권(12): pp. 102-108
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The Communication Network Within the Crowd
Ming Yin, Mary L. Gray, Siddharth Suri, Jennifer Wortman Vaughan
25th International World Wide Web Conference (WWW) 2016 | April 2016
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The Possibilities and Limitations of Private Prediction Markets
Rachel Cummings, David Pennock, Jennifer Wortman Vaughan
17th ACM Conference on Economics and Computation (EC 2016) | April 2016
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Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Journal of Artificial Intelligence Research | February 2016, 제 55 권
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Bounded Rationality in Wagering Mechanisms
David Pennock, Vasilis Syrgkanis, Jennifer Wortman Vaughan
32nd Conference on Uncertainty in Artificial Intelligence | January 2016
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Belief Aggregation with Automated Market Makers
Rajiv Sethi, Jennifer Wortman Vaughan
Computational Economics | June 2015
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Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets
Hoda Heidari, Sébastien Lahaie, David Pennock, Jennifer Wortman Vaughan
June 2015
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Incentivizing High Quality Crowdwork
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan
Twenty-Fourth International World Wide Web Conference | May 2015
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Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
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Removing Arbitrage from Wagering Mechanisms
Yiling Chen, Nikhil Devanur, David Pennock, Jennifer Wortman Vaughan
Fifteenth ACM Conference on Economics and Computation | June 2014
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An Axiomatic Characterization of Wagering Mechanisms
Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, David Pennock, Nicolas S. Lambert, John Langford, Yoav Shoham
Journal of Economic Theory | March 2014, 제 156 권: pp. 389-416
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A General Volume-Parameterized Market Making Framework
Jacob Abernethy, Rafael Frongillo, Xiaolong Li, Jennifer Wortman Vaughan
ACM Conference on Electronic Commerce | January 2014
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Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
15th ACM Conf. on Economics and Computation (EC) | January 2014
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Online Decision Making in Crowdsourcing Markets: Theoretical Challenges
Aleksandrs Slivkins, Jennifer Wortman Vaughan
ACM SIGecom Exchanges | December 2013, 제 12 권(2): pp. 4-23
Position paper and survey.
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Computational social science and social computing
Winter Mason, Jennifer Wortman Vaughan, Hanna Wallach
Mach Learn | November 2013, 제 95 권: pp. 257-260
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Adaptive Task Assignment for Crowdsourced Classification
Chien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan
30th International Conference on Machine Learning (ICML) | May 2013
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An Axiomatic Characterization of Adaptive-Liquidity Market Makers
Xiaolong Li, Jennifer Wortman Vaughan
Fourteenth ACM Conference on Electronic Commerce (EC) | May 2013
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Efficient Market Making via Convex Optimization, and a Connection to Online Learning
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation | May 2013, 제 1 권
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Cost Function Market Makers for Measurable Spaces
Yiling Chen, Michael Ruberry, Jennifer Wortman Vaughan
Fourteenth ACM Conference on Electronic Commerce (EC) | January 2013
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Designing Informative Securities
Yiling Chen, Mike Ruberry, Jennifer Wortman Vaughan
28th Conference on Uncertainty in Artificial Intelligence (UAI) | January 2012
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Online Task Assignment in Crowdsourcing Markets
Chien-Ju Ho, Jennifer Wortman Vaughan
Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI) | January 2012
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An Optimization-Based Framework for Automated Market-Making
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Twelfth ACM Conference on Electronic Commerce (EC) | January 2011
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“Helping Me Versus Doing It for Me”: Designing for Agency in LLM-Infused Writing Tools for Science Journalism
Sachita Nishal, Mina Lee, Nicholas Diakopoulos, Jennifer Wortman Vaughan
CHI Conference on Human Factors in Computing Systems | April 2026
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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
ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2025) | November 2025
Honorable Mention
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Position: To Make Text-to-Image Models that Work for Marginalized Communities, We Need New Measurement Practices for the Long Tail
Nari Johnson, Hamna ., Deepthi Sudharsan, Theo Holroyd, Samantha Dalal, Siobhan Mackenzie Hall, Jennifer Wortman Vaughan, Daniela Massiceti, Cecily Morrison
July 2025
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Examining the Expanding Role of Synthetic Data Throughout the AI Development Pipeline
Shivani Kapania, Stephanie Ballard, Alex Kessler, Jennifer Wortman Vaughan
8th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2025) | June 2025
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Canvil: Designerly Adaptation for LLM-Powered User Experiences
K. Feng, Q. Vera Liao, Ziang Xiao, Jennifer Wortman Vaughan, Amy Zhang, David W. McDonald
CHI 2025 | April 2025
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Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao, Tania Lombrozo, Olga Russakovsky
CHI 2025 | April 2025
Honorable Mention, CHI 2025
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Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, Jennifer Wortman Vaughan
ToCHI | April 2025, 제 32 권(1)
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Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, A. Feder Cooper, Angelina Wang, Chad Atalla, Solon Barocas, Su Lin Blodgett, Alex Chouldechova, Emily Corvi, Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Nick Pangakis, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs
ICML 2025 | January 2025
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Machine Unlearning Doesn’t Do What You Think: Lessons for Generative AI Policy, Research, and Practice
A. F. Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu Liu, Alex Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, N. Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, A. Cyphert, Mark A. Lemley, danah boyd, Jennifer Wortman Vaughan, M. Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna Wallach, Nicolas Papernot, Katherine Lee
NeurIPS 2025 | December 2024
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Microsoft New Future of Work Report 2024
Jenna Butler, Mihaela Vorvoreanu, Rebecca Janssen, Abigail Sellen, Nicole Immorlica, Adam Troy, Advait Sarkar, Alex Farach, Alex Chouldechova, Alexandra Olteanu, Alexia Cambon, Arjun Radhakrishna, Asta Roseway, Ben Zorn, Brent Hecht, Daniel G. Goldstein, Dhruv Joshi, Ed Cutrell, Emre Kiciman, Gonzalo Ramos, Gustavo Soares, Hanna Wallach, Ian Drosos, Jack Williams (johnwilliams), Jacki O'Neill, Jake Hofman, Jaime Teevan, Javier Hernandez, Jennifer Wortman Vaughan, Jina Suh, John Tang, Justin Edwards, Kalika Bali, Kori Inkpen, Krishna Madhavan, Laylah Bulman, Leon Reicherts, Lev Tankelevitch, Longqi Yang, Martez Mott, Millicent Ochieng, Mercy Muchai, Nancy Baym, Najeeb Abdulhamid, Nicolai Marquardt, Ken Hinckley, Michael Bentley, Dave Brown, Hugo Romat, Nathalie Henry Riche, Samuel Maina, Shamsi Iqbal, Siân Lindley, Stephanie Nyairo, Su Lin Blodgett, Sumit Gulwani, Sunayana Sitaram, Vu Le
MSR-TR-2024-56 | December 2024
글쓴이 Microsoft
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Challenges in Human-Agent Communication
Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi, Eric Horvitz, Adam Fourney, Hussein Mozannar, Victor Dibia, Daniel S. Weld
MSR-TR-2024-53 | December 2024
글쓴이 Microsoft
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Dimensions of Generative AI Evaluation Design
Alex Dow, Jennifer Wortman Vaughan, Solon Barocas, Chad Atalla, Alex Chouldechova, Hanna Wallach
November 2024
-
Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, Nick Pangakis, A. Feder Cooper, Angelina Wang, Solon Barocas, Alex Chouldechova, Chad Atalla, Su Lin Blodgett, Emily Corvi, Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs
November 2024
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(De)Noise: Moderating the Inconsistency Between Human Decision-Makers
Nina Grgić-Hlača, Junaid Ali, Krishna P. Gummadi, Jennifer Wortman Vaughan
ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2024) | November 2024
-
“I’m Not Sure, But…”: Examining the Impact of Large Language Models’ Uncertainty Expression on User Reliance and Trust
Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, Jennifer Wortman Vaughan
7th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2024) | June 2024
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The CLeAR Documentation Framework for AI Transparency: Recommendations for Practitioners & Context for Policymakers
Kasia Chmielinski, Sarah Newman, Chris N. Kranzinger, Michael Hind, Jennifer Wortman Vaughan, Margaret Mitchell, Julia Stoyanovich, Angelina McMillan-Major, Emily McReynolds, Kathleen Esfahany, Mary L. Gray, Audrey Chang, Maui Hudson
April 2024
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Tinker, Tailor, Configure, Customize: The Articulation Work of Customizing AI Fairness Checklists
Michael Madaio, Jingya Chen, Hanna Wallach, Jennifer Wortman Vaughan
Computer-Supported Cooperative Work | April 2024
-
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
Q. Vera Liao, Jennifer Wortman Vaughan
Harvard Data Science Review | February 2024
-
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
Proc. ACM Hum.-Comput. Interact. | October 2023, 제 7 권
Article 370
-
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | April 2023
-
GAM Coach: Towards Interactive and User-Centered Algorithmic Recourse
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | February 2023
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Understanding Machine Learning Practitioners’ Data Documentation Perceptions, Needs, Challenges, and Desiderata
Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, Jennifer Wortman Vaughan
2022 Conference on Computer Supported Cooperative Work | November 2022
-
Generation Probabilities are Not Enough: Improving Error Highlighting for AI Code Suggestions
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, Jennifer Wortman Vaughan
NeurIPS Workshop on Human-Centered AI | October 2022
-
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
KDD 2022 | July 2022
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REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, Jennifer Wortman Vaughan
FAccT 2022 | May 2022
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Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support
Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, Hanna Wallach
25th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2022) | February 2022
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A Human-Centered Agenda for Intelligible Machine Learning
Jennifer Wortman Vaughan, Hanna Wallach
In Machines We Trust: Perspectives on Dependable AI | 글쓴이 MIT Press | 2021
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GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2021 Neural Information Processing Systems | December 2021
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Summarize with Caution: Comparing Global Feature Attributions
Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna Wallach, Jennifer Wortman Vaughan
Bulletin of the IEEE Computer Society Technical Committee on Data Engineering | December 2021
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Datasheets for Datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford
Communications of the ACM | December 2021, 제 64 권(12): pp. 86-92
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From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna Wallach, Jennifer Wortman Vaughan
HCOMP 2021 | September 2021
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Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, 제 67 권(7): pp. 30-32
https://doi.org/10.1145/3466612
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Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2021 | May 2021
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Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, Duncan Wadsworth, Hanna Wallach
AIES 2021 | May 2021
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No-Regret and Incentive-Compatible Online Learning
Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan
ICML 2020 | July 2020
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A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition
Forough Poursabzi-Sangdeh, Samira Samadi, Jennifer Wortman Vaughan, Hanna Wallach
April 2020
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
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Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI
Michael Madaio, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach
CHI Conference on Human Factors in Computing Systems | March 2020
CHI 2020 Best Paper Award
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Weight of Evidence as a Basis for Human-Oriented Explanations
David Alvarez-Melis, Hal Daumé III, Jennifer Wortman Vaughan, Hanna Wallach
Workshop on Human-Centric Machine Learning at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. | October 2019
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Toward Fairness in AI for People with Disabilities: A Research Roadmap
Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna Wallach, Meredith Ringel Morris
ASSETS 2019 Workshop on AI Fairness for People with Disabilities | October 2019
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Truthful Aggregation of Budget Proposals
Rupert Freeman, David Pennock, Dominik Peters, Jennifer Wortman Vaughan
Twentieth ACM Conference on Economics and Computation | June 2019
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Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
Honorable Mention Award at CHI 2019
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The Disparate Effects of Strategic Manipulation
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
2nd ACM Conference on Fairness, Accountability, and Transparency | January 2019
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Group Fairness for Indivisible Goods Allocation
Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan
AAAI 2019 | January 2019
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An Equivalence Between Wagering and Fair-Division Mechanisms
Rupert Freeman, David Pennock, Jennifer Wortman Vaughan
AAAI 2019 | January 2019
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Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets
Hoda Heidari, Sebastien Lahaie, David Pennock, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation (TEAC) - Special Issue on EC'15 | November 2018, 제 6 권(3–4)
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Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, 제 18 권(193): pp. 1-46
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Incentive-Compatible Forecasting Competitions
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David Pennock, Andreas Krause
Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018) | April 2018
-
The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
31st Annual Conference on Learning Theory | February 2018
-
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
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The Double Clinching Auction for Wagering
Rupert Freeman, David Pennock, Jennifer Wortman Vaughan
EC'17, Cambridge, Massachusetts, USA | June 2017
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A Decomposition of Forecast Error in Prediction Markets
Miro Dudík, Sébastien Lahaie, Ryan Rogers, Jennifer Wortman Vaughan
31st Conference on Neural Information Processing Systems (NIPS 2017) | February 2017
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Mathematical Foundations for Social Computing
Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, Jennifer Wortman Vaughan
Communications of the ACM | December 2016, 제 59 권(12): pp. 102-108
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The Communication Network Within the Crowd
Ming Yin, Mary L. Gray, Siddharth Suri, Jennifer Wortman Vaughan
25th International World Wide Web Conference (WWW) 2016 | April 2016
-
The Possibilities and Limitations of Private Prediction Markets
Rachel Cummings, David Pennock, Jennifer Wortman Vaughan
17th ACM Conference on Economics and Computation (EC 2016) | April 2016
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Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Journal of Artificial Intelligence Research | February 2016, 제 55 권
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Belief Aggregation with Automated Market Makers
Rajiv Sethi, Jennifer Wortman Vaughan
Computational Economics | June 2015
-
Incentivizing High Quality Crowdwork
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan
Twenty-Fourth International World Wide Web Conference | May 2015
-
Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
-
Removing Arbitrage from Wagering Mechanisms
Yiling Chen, Nikhil Devanur, David Pennock, Jennifer Wortman Vaughan
Fifteenth ACM Conference on Economics and Computation | June 2014
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An Axiomatic Characterization of Wagering Mechanisms
Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, David Pennock, Nicolas S. Lambert, John Langford, Yoav Shoham
Journal of Economic Theory | March 2014, 제 156 권: pp. 389-416
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A General Volume-Parameterized Market Making Framework
Jacob Abernethy, Rafael Frongillo, Xiaolong Li, Jennifer Wortman Vaughan
ACM Conference on Electronic Commerce | January 2014
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Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
15th ACM Conf. on Economics and Computation (EC) | January 2014
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Online Decision Making in Crowdsourcing Markets: Theoretical Challenges
Aleksandrs Slivkins, Jennifer Wortman Vaughan
ACM SIGecom Exchanges | December 2013, 제 12 권(2): pp. 4-23
Position paper and survey.
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Adaptive Task Assignment for Crowdsourced Classification
Chien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan
30th International Conference on Machine Learning (ICML) | May 2013
-
An Axiomatic Characterization of Adaptive-Liquidity Market Makers
Xiaolong Li, Jennifer Wortman Vaughan
Fourteenth ACM Conference on Electronic Commerce (EC) | May 2013
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Efficient Market Making via Convex Optimization, and a Connection to Online Learning
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation | May 2013, 제 1 권
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Cost Function Market Makers for Measurable Spaces
Yiling Chen, Michael Ruberry, Jennifer Wortman Vaughan
Fourteenth ACM Conference on Electronic Commerce (EC) | January 2013
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Online Task Assignment in Crowdsourcing Markets
Chien-Ju Ho, Jennifer Wortman Vaughan
Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI) | January 2012
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Designing Informative Securities
Yiling Chen, Mike Ruberry, Jennifer Wortman Vaughan
28th Conference on Uncertainty in Artificial Intelligence (UAI) | January 2012
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An Optimization-Based Framework for Automated Market-Making
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Twelfth ACM Conference on Electronic Commerce (EC) | January 2011
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“Helping Me Versus Doing It for Me”: Designing for Agency in LLM-Infused Writing Tools for Science Journalism
Sachita Nishal, Mina Lee, Nicholas Diakopoulos, Jennifer Wortman Vaughan
CHI Conference on Human Factors in Computing Systems | April 2026
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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
ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2025) | November 2025
Honorable Mention
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Position: To Make Text-to-Image Models that Work for Marginalized Communities, We Need New Measurement Practices for the Long Tail
Nari Johnson, Hamna ., Deepthi Sudharsan, Theo Holroyd, Samantha Dalal, Siobhan Mackenzie Hall, Jennifer Wortman Vaughan, Daniela Massiceti, Cecily Morrison
July 2025
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Canvil: Designerly Adaptation for LLM-Powered User Experiences
K. Feng, Q. Vera Liao, Ziang Xiao, Jennifer Wortman Vaughan, Amy Zhang, David W. McDonald
CHI 2025 | April 2025
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Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao, Tania Lombrozo, Olga Russakovsky
CHI 2025 | April 2025
Honorable Mention, CHI 2025
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Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, Jennifer Wortman Vaughan
ToCHI | April 2025, 제 32 권(1)
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Microsoft New Future of Work Report 2024
Jenna Butler, Mihaela Vorvoreanu, Rebecca Janssen, Abigail Sellen, Nicole Immorlica, Adam Troy, Advait Sarkar, Alex Farach, Alex Chouldechova, Alexandra Olteanu, Alexia Cambon, Arjun Radhakrishna, Asta Roseway, Ben Zorn, Brent Hecht, Daniel G. Goldstein, Dhruv Joshi, Ed Cutrell, Emre Kiciman, Gonzalo Ramos, Gustavo Soares, Hanna Wallach, Ian Drosos, Jack Williams (johnwilliams), Jacki O'Neill, Jake Hofman, Jaime Teevan, Javier Hernandez, Jennifer Wortman Vaughan, Jina Suh, John Tang, Justin Edwards, Kalika Bali, Kori Inkpen, Krishna Madhavan, Laylah Bulman, Leon Reicherts, Lev Tankelevitch, Longqi Yang, Martez Mott, Millicent Ochieng, Mercy Muchai, Nancy Baym, Najeeb Abdulhamid, Nicolai Marquardt, Ken Hinckley, Michael Bentley, Dave Brown, Hugo Romat, Nathalie Henry Riche, Samuel Maina, Shamsi Iqbal, Siân Lindley, Stephanie Nyairo, Su Lin Blodgett, Sumit Gulwani, Sunayana Sitaram, Vu Le
MSR-TR-2024-56 | December 2024
글쓴이 Microsoft
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Challenges in Human-Agent Communication
Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi, Eric Horvitz, Adam Fourney, Hussein Mozannar, Victor Dibia, Daniel S. Weld
MSR-TR-2024-53 | December 2024
글쓴이 Microsoft
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(De)Noise: Moderating the Inconsistency Between Human Decision-Makers
Nina Grgić-Hlača, Junaid Ali, Krishna P. Gummadi, Jennifer Wortman Vaughan
ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2024) | November 2024
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AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
Q. Vera Liao, Jennifer Wortman Vaughan
Harvard Data Science Review | February 2024
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Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
Proc. ACM Hum.-Comput. Interact. | October 2023, 제 7 권
Article 370
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Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | April 2023
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GAM Coach: Towards Interactive and User-Centered Algorithmic Recourse
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | February 2023
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Understanding Machine Learning Practitioners’ Data Documentation Perceptions, Needs, Challenges, and Desiderata
Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, Jennifer Wortman Vaughan
2022 Conference on Computer Supported Cooperative Work | November 2022
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Generation Probabilities are Not Enough: Improving Error Highlighting for AI Code Suggestions
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, Jennifer Wortman Vaughan
NeurIPS Workshop on Human-Centered AI | October 2022
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GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2021 Neural Information Processing Systems | December 2021
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Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, 제 67 권(7): pp. 30-32
https://doi.org/10.1145/3466612
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
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Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI
Michael Madaio, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach
CHI Conference on Human Factors in Computing Systems | March 2020
CHI 2020 Best Paper Award
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Toward Fairness in AI for People with Disabilities: A Research Roadmap
Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna Wallach, Meredith Ringel Morris
ASSETS 2019 Workshop on AI Fairness for People with Disabilities | October 2019
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Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Yin, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2019 | May 2019
Honorable Mention Award at CHI 2019
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
Honorable Mention Award at CHI 2019
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Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, 제 18 권(193): pp. 1-46
-
-
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Position: To Make Text-to-Image Models that Work for Marginalized Communities, We Need New Measurement Practices for the Long Tail
Nari Johnson, Hamna ., Deepthi Sudharsan, Theo Holroyd, Samantha Dalal, Siobhan Mackenzie Hall, Jennifer Wortman Vaughan, Daniela Massiceti, Cecily Morrison
July 2025
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Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, A. Feder Cooper, Angelina Wang, Chad Atalla, Solon Barocas, Su Lin Blodgett, Alex Chouldechova, Emily Corvi, Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Nick Pangakis, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs
ICML 2025 | January 2025
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Microsoft New Future of Work Report 2024
Jenna Butler, Mihaela Vorvoreanu, Rebecca Janssen, Abigail Sellen, Nicole Immorlica, Adam Troy, Advait Sarkar, Alex Farach, Alex Chouldechova, Alexandra Olteanu, Alexia Cambon, Arjun Radhakrishna, Asta Roseway, Ben Zorn, Brent Hecht, Daniel G. Goldstein, Dhruv Joshi, Ed Cutrell, Emre Kiciman, Gonzalo Ramos, Gustavo Soares, Hanna Wallach, Ian Drosos, Jack Williams (johnwilliams), Jacki O'Neill, Jake Hofman, Jaime Teevan, Javier Hernandez, Jennifer Wortman Vaughan, Jina Suh, John Tang, Justin Edwards, Kalika Bali, Kori Inkpen, Krishna Madhavan, Laylah Bulman, Leon Reicherts, Lev Tankelevitch, Longqi Yang, Martez Mott, Millicent Ochieng, Mercy Muchai, Nancy Baym, Najeeb Abdulhamid, Nicolai Marquardt, Ken Hinckley, Michael Bentley, Dave Brown, Hugo Romat, Nathalie Henry Riche, Samuel Maina, Shamsi Iqbal, Siân Lindley, Stephanie Nyairo, Su Lin Blodgett, Sumit Gulwani, Sunayana Sitaram, Vu Le
MSR-TR-2024-56 | December 2024
글쓴이 Microsoft
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Dimensions of Generative AI Evaluation Design
Alex Dow, Jennifer Wortman Vaughan, Solon Barocas, Chad Atalla, Alex Chouldechova, Hanna Wallach
November 2024
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Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, Nick Pangakis, A. Feder Cooper, Angelina Wang, Solon Barocas, Alex Chouldechova, Chad Atalla, Su Lin Blodgett, Emily Corvi, Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs
November 2024
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Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, 제 67 권(7): pp. 30-32
https://doi.org/10.1145/3466612
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Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2021 | May 2021
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Using Search Queries to Understand Health Information Needs in Africa
Rediet Abebe, Shawndra Hill, Jennifer Wortman Vaughan, Peter M. Small, H. Andrew Schwartz
Proceedings of the International AAAI Conference on Web and Social Media | June 2019
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Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, 제 18 권(193): pp. 1-46
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The Communication Network Within the Crowd
Ming Yin, Mary L. Gray, Siddharth Suri, Jennifer Wortman Vaughan
25th International World Wide Web Conference (WWW) 2016 | April 2016
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Computational social science and social computing
Winter Mason, Jennifer Wortman Vaughan, Hanna Wallach
Mach Learn | November 2013, 제 95 권: pp. 257-260
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Microsoft New Future of Work Report 2024
Jenna Butler, Mihaela Vorvoreanu, Rebecca Janssen, Abigail Sellen, Nicole Immorlica, Adam Troy, Advait Sarkar, Alex Farach, Alex Chouldechova, Alexandra Olteanu, Alexia Cambon, Arjun Radhakrishna, Asta Roseway, Ben Zorn, Brent Hecht, Daniel G. Goldstein, Dhruv Joshi, Ed Cutrell, Emre Kiciman, Gonzalo Ramos, Gustavo Soares, Hanna Wallach, Ian Drosos, Jack Williams (johnwilliams), Jacki O'Neill, Jake Hofman, Jaime Teevan, Javier Hernandez, Jennifer Wortman Vaughan, Jina Suh, John Tang, Justin Edwards, Kalika Bali, Kori Inkpen, Krishna Madhavan, Laylah Bulman, Leon Reicherts, Lev Tankelevitch, Longqi Yang, Martez Mott, Millicent Ochieng, Mercy Muchai, Nancy Baym, Najeeb Abdulhamid, Nicolai Marquardt, Ken Hinckley, Michael Bentley, Dave Brown, Hugo Romat, Nathalie Henry Riche, Samuel Maina, Shamsi Iqbal, Siân Lindley, Stephanie Nyairo, Su Lin Blodgett, Sumit Gulwani, Sunayana Sitaram, Vu Le
MSR-TR-2024-56 | December 2024
글쓴이 Microsoft
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The CLeAR Documentation Framework for AI Transparency: Recommendations for Practitioners & Context for Policymakers
Kasia Chmielinski, Sarah Newman, Chris N. Kranzinger, Michael Hind, Jennifer Wortman Vaughan, Margaret Mitchell, Julia Stoyanovich, Angelina McMillan-Major, Emily McReynolds, Kathleen Esfahany, Mary L. Gray, Audrey Chang, Maui Hudson
April 2024
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Microsoft New Future of Work Report 2024
Jenna Butler, Mihaela Vorvoreanu, Rebecca Janssen, Abigail Sellen, Nicole Immorlica, Adam Troy, Advait Sarkar, Alex Farach, Alex Chouldechova, Alexandra Olteanu, Alexia Cambon, Arjun Radhakrishna, Asta Roseway, Ben Zorn, Brent Hecht, Daniel G. Goldstein, Dhruv Joshi, Ed Cutrell, Emre Kiciman, Gonzalo Ramos, Gustavo Soares, Hanna Wallach, Ian Drosos, Jack Williams (johnwilliams), Jacki O'Neill, Jake Hofman, Jaime Teevan, Javier Hernandez, Jennifer Wortman Vaughan, Jina Suh, John Tang, Justin Edwards, Kalika Bali, Kori Inkpen, Krishna Madhavan, Laylah Bulman, Leon Reicherts, Lev Tankelevitch, Longqi Yang, Martez Mott, Millicent Ochieng, Mercy Muchai, Nancy Baym, Najeeb Abdulhamid, Nicolai Marquardt, Ken Hinckley, Michael Bentley, Dave Brown, Hugo Romat, Nathalie Henry Riche, Samuel Maina, Shamsi Iqbal, Siân Lindley, Stephanie Nyairo, Su Lin Blodgett, Sumit Gulwani, Sunayana Sitaram, Vu Le
MSR-TR-2024-56 | December 2024
글쓴이 Microsoft
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Truthful Aggregation of Budget Proposals
Rupert Freeman, David Pennock, Dominik Peters, Jennifer Wortman Vaughan
Twentieth ACM Conference on Economics and Computation | June 2019
-
The Disparate Effects of Strategic Manipulation
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
2nd ACM Conference on Fairness, Accountability, and Transparency | January 2019
-
Group Fairness for Indivisible Goods Allocation
Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan
AAAI 2019 | January 2019
-
An Equivalence Between Wagering and Fair-Division Mechanisms
Rupert Freeman, David Pennock, Jennifer Wortman Vaughan
AAAI 2019 | January 2019
-
Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets
Hoda Heidari, Sebastien Lahaie, David Pennock, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation (TEAC) - Special Issue on EC'15 | November 2018, 제 6 권(3–4)
-
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, 제 18 권(193): pp. 1-46
-
Incentive-Compatible Forecasting Competitions
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David Pennock, Andreas Krause
Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018) | April 2018
-
The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
31st Annual Conference on Learning Theory | February 2018
-
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
-
A Decomposition of Forecast Error in Prediction Markets
Miro Dudík, Sébastien Lahaie, Ryan Rogers, Jennifer Wortman Vaughan
31st Conference on Neural Information Processing Systems (NIPS 2017) | February 2017
-
Mathematical Foundations for Social Computing
Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, Jennifer Wortman Vaughan
Communications of the ACM | December 2016, 제 59 권(12): pp. 102-108
-
The Possibilities and Limitations of Private Prediction Markets
Rachel Cummings, David Pennock, Jennifer Wortman Vaughan
17th ACM Conference on Economics and Computation (EC 2016) | April 2016
-
Bounded Rationality in Wagering Mechanisms
David Pennock, Vasilis Syrgkanis, Jennifer Wortman Vaughan
32nd Conference on Uncertainty in Artificial Intelligence | January 2016
-
Belief Aggregation with Automated Market Makers
Rajiv Sethi, Jennifer Wortman Vaughan
Computational Economics | June 2015
-
Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets
Hoda Heidari, Sébastien Lahaie, David Pennock, Jennifer Wortman Vaughan
June 2015
-
Incentivizing High Quality Crowdwork
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan
Twenty-Fourth International World Wide Web Conference | May 2015
-
Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
-
Removing Arbitrage from Wagering Mechanisms
Yiling Chen, Nikhil Devanur, David Pennock, Jennifer Wortman Vaughan
Fifteenth ACM Conference on Economics and Computation | June 2014
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An Axiomatic Characterization of Wagering Mechanisms
Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, David Pennock, Nicolas S. Lambert, John Langford, Yoav Shoham
Journal of Economic Theory | March 2014, 제 156 권: pp. 389-416
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A General Volume-Parameterized Market Making Framework
Jacob Abernethy, Rafael Frongillo, Xiaolong Li, Jennifer Wortman Vaughan
ACM Conference on Electronic Commerce | January 2014
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Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
15th ACM Conf. on Economics and Computation (EC) | January 2014
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An Axiomatic Characterization of Adaptive-Liquidity Market Makers
Xiaolong Li, Jennifer Wortman Vaughan
Fourteenth ACM Conference on Electronic Commerce (EC) | May 2013
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Efficient Market Making via Convex Optimization, and a Connection to Online Learning
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation | May 2013, 제 1 권
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Cost Function Market Makers for Measurable Spaces
Yiling Chen, Michael Ruberry, Jennifer Wortman Vaughan
Fourteenth ACM Conference on Electronic Commerce (EC) | January 2013
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Designing Informative Securities
Yiling Chen, Mike Ruberry, Jennifer Wortman Vaughan
28th Conference on Uncertainty in Artificial Intelligence (UAI) | January 2012
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An Optimization-Based Framework for Automated Market-Making
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Twelfth ACM Conference on Electronic Commerce (EC) | January 2011
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Microsoft New Future of Work Report 2024
Jenna Butler, Mihaela Vorvoreanu, Rebecca Janssen, Abigail Sellen, Nicole Immorlica, Adam Troy, Advait Sarkar, Alex Farach, Alex Chouldechova, Alexandra Olteanu, Alexia Cambon, Arjun Radhakrishna, Asta Roseway, Ben Zorn, Brent Hecht, Daniel G. Goldstein, Dhruv Joshi, Ed Cutrell, Emre Kiciman, Gonzalo Ramos, Gustavo Soares, Hanna Wallach, Ian Drosos, Jack Williams (johnwilliams), Jacki O'Neill, Jake Hofman, Jaime Teevan, Javier Hernandez, Jennifer Wortman Vaughan, Jina Suh, John Tang, Justin Edwards, Kalika Bali, Kori Inkpen, Krishna Madhavan, Laylah Bulman, Leon Reicherts, Lev Tankelevitch, Longqi Yang, Martez Mott, Millicent Ochieng, Mercy Muchai, Nancy Baym, Najeeb Abdulhamid, Nicolai Marquardt, Ken Hinckley, Michael Bentley, Dave Brown, Hugo Romat, Nathalie Henry Riche, Samuel Maina, Shamsi Iqbal, Siân Lindley, Stephanie Nyairo, Su Lin Blodgett, Sumit Gulwani, Sunayana Sitaram, Vu Le
MSR-TR-2024-56 | December 2024
글쓴이 Microsoft
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Toward Fairness in AI for People with Disabilities: A Research Roadmap
Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna Wallach, Meredith Ringel Morris
ASSETS 2019 Workshop on AI Fairness for People with Disabilities | October 2019
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The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
31st Annual Conference on Learning Theory | February 2018
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Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
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A Decomposition of Forecast Error in Prediction Markets
Miro Dudík, Sébastien Lahaie, Ryan Rogers, Jennifer Wortman Vaughan
31st Conference on Neural Information Processing Systems (NIPS 2017) | February 2017
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Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Journal of Artificial Intelligence Research | February 2016, 제 55 권
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Bounded Rationality in Wagering Mechanisms
David Pennock, Vasilis Syrgkanis, Jennifer Wortman Vaughan
32nd Conference on Uncertainty in Artificial Intelligence | January 2016
-
A General Volume-Parameterized Market Making Framework
Jacob Abernethy, Rafael Frongillo, Xiaolong Li, Jennifer Wortman Vaughan
ACM Conference on Electronic Commerce | January 2014
-
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
15th ACM Conf. on Economics and Computation (EC) | January 2014
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Online Decision Making in Crowdsourcing Markets: Theoretical Challenges
Aleksandrs Slivkins, Jennifer Wortman Vaughan
ACM SIGecom Exchanges | December 2013, 제 12 권(2): pp. 4-23
Position paper and survey.
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Adaptive Task Assignment for Crowdsourced Classification
Chien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan
30th International Conference on Machine Learning (ICML) | May 2013
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An Axiomatic Characterization of Adaptive-Liquidity Market Makers
Xiaolong Li, Jennifer Wortman Vaughan
Fourteenth ACM Conference on Electronic Commerce (EC) | May 2013
-
Efficient Market Making via Convex Optimization, and a Connection to Online Learning
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation | May 2013, 제 1 권
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Cost Function Market Makers for Measurable Spaces
Yiling Chen, Michael Ruberry, Jennifer Wortman Vaughan
Fourteenth ACM Conference on Electronic Commerce (EC) | January 2013
-
Online Task Assignment in Crowdsourcing Markets
Chien-Ju Ho, Jennifer Wortman Vaughan
Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI) | January 2012
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An Optimization-Based Framework for Automated Market-Making
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Twelfth ACM Conference on Electronic Commerce (EC) | January 2011
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“Helping Me Versus Doing It for Me”: Designing for Agency in LLM-Infused Writing Tools for Science Journalism
Sachita Nishal, Mina Lee, Nicholas Diakopoulos, Jennifer Wortman Vaughan
CHI Conference on Human Factors in Computing Systems | April 2026
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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
ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2025) | November 2025
Honorable Mention
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Examining the Expanding Role of Synthetic Data Throughout the AI Development Pipeline
Shivani Kapania, Stephanie Ballard, Alex Kessler, Jennifer Wortman Vaughan
8th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2025) | June 2025
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Canvil: Designerly Adaptation for LLM-Powered User Experiences
K. Feng, Q. Vera Liao, Ziang Xiao, Jennifer Wortman Vaughan, Amy Zhang, David W. McDonald
CHI 2025 | April 2025
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Fostering Appropriate Reliance on Large Language Models: The Role of Explanations, Sources, and Inconsistencies
Sunnie S. Y. Kim, Jennifer Wortman Vaughan, Q. Vera Liao, Tania Lombrozo, Olga Russakovsky
CHI 2025 | April 2025
Honorable Mention, CHI 2025
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Position: Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, A. Feder Cooper, Angelina Wang, Chad Atalla, Solon Barocas, Su Lin Blodgett, Alex Chouldechova, Emily Corvi, Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Nick Pangakis, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs
ICML 2025 | January 2025
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Machine Unlearning Doesn’t Do What You Think: Lessons for Generative AI Policy, Research, and Practice
A. F. Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski, Katja Filippova, Ken Ziyu Liu, Alex Chouldechova, Jamie Hayes, Yangsibo Huang, Niloofar Mireshghallah, Ilia Shumailov, Eleni Triantafillou, Peter Kairouz, N. Mitchell, Percy Liang, Daniel E. Ho, Yejin Choi, Sanmi Koyejo, Fernando Delgado, James Grimmelmann, Vitaly Shmatikov, Christopher De Sa, Solon Barocas, A. Cyphert, Mark A. Lemley, danah boyd, Jennifer Wortman Vaughan, M. Brundage, David Bau, Seth Neel, Abigail Z. Jacobs, Andreas Terzis, Hanna Wallach, Nicolas Papernot, Katherine Lee
NeurIPS 2025 | December 2024
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(De)Noise: Moderating the Inconsistency Between Human Decision-Makers
Nina Grgić-Hlača, Junaid Ali, Krishna P. Gummadi, Jennifer Wortman Vaughan
ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2024) | November 2024
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“I’m Not Sure, But…”: Examining the Impact of Large Language Models’ Uncertainty Expression on User Reliance and Trust
Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, Jennifer Wortman Vaughan
7th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2024) | June 2024
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Tinker, Tailor, Configure, Customize: The Articulation Work of Customizing AI Fairness Checklists
Michael Madaio, Jingya Chen, Hanna Wallach, Jennifer Wortman Vaughan
Computer-Supported Cooperative Work | April 2024
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Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience
Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | April 2023
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GAM Coach: Towards Interactive and User-Centered Algorithmic Recourse
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023) | February 2023
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Understanding Machine Learning Practitioners’ Data Documentation Perceptions, Needs, Challenges, and Desiderata
Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, Jennifer Wortman Vaughan
2022 Conference on Computer Supported Cooperative Work | November 2022
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Generation Probabilities are Not Enough: Improving Error Highlighting for AI Code Suggestions
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, Jennifer Wortman Vaughan
NeurIPS Workshop on Human-Centered AI | October 2022
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Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
KDD 2022 | July 2022
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REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, Jennifer Wortman Vaughan
FAccT 2022 | May 2022
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Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support
Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, Hanna Wallach
25th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2022) | February 2022
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GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
2021 Neural Information Processing Systems | December 2021
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From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna Wallach, Jennifer Wortman Vaughan
HCOMP 2021 | September 2021
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Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake Hofman, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2021 | May 2021
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Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, Duncan Wadsworth, Hanna Wallach
AIES 2021 | May 2021
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No-Regret and Incentive-Compatible Online Learning
Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan
ICML 2020 | July 2020
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Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, Jennifer Wortman Vaughan
CHI 2020 | April 2020
CHI 2020 Honorable Mention Award
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Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI
Michael Madaio, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach
CHI Conference on Human Factors in Computing Systems | March 2020
CHI 2020 Best Paper Award
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Weight of Evidence as a Basis for Human-Oriented Explanations
David Alvarez-Melis, Hal Daumé III, Jennifer Wortman Vaughan, Hanna Wallach
Workshop on Human-Centric Machine Learning at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada. | October 2019
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Toward Fairness in AI for People with Disabilities: A Research Roadmap
Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna Wallach, Meredith Ringel Morris
ASSETS 2019 Workshop on AI Fairness for People with Disabilities | October 2019
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Using Search Queries to Understand Health Information Needs in Africa
Rediet Abebe, Shawndra Hill, Jennifer Wortman Vaughan, Peter M. Small, H. Andrew Schwartz
Proceedings of the International AAAI Conference on Web and Social Media | June 2019
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Truthful Aggregation of Budget Proposals
Rupert Freeman, David Pennock, Dominik Peters, Jennifer Wortman Vaughan
Twentieth ACM Conference on Economics and Computation | June 2019
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Improving fairness in machine learning systems: What do industry practitioners need?
Ken Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miro Dudík, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | June 2019
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Yin, Jennifer Wortman Vaughan, Hanna Wallach
CHI 2019 | May 2019
Honorable Mention Award at CHI 2019
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Ying, Jennifer Wortman Vaughan, Hanna Wallach
2019 ACM CHI Conference on Human Factors in Computing Systems | May 2019
Honorable Mention Award at CHI 2019
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The Disparate Effects of Strategic Manipulation
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
2nd ACM Conference on Fairness, Accountability, and Transparency | January 2019
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Group Fairness for Indivisible Goods Allocation
Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan
AAAI 2019 | January 2019
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An Equivalence Between Wagering and Fair-Division Mechanisms
Rupert Freeman, David Pennock, Jennifer Wortman Vaughan
AAAI 2019 | January 2019
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Incentive-Compatible Forecasting Competitions
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David Pennock, Andreas Krause
Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018) | April 2018
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The Externalities of Exploration and How Data Diversity Helps Exploitation
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
31st Annual Conference on Learning Theory | February 2018
-
Oracle-Efficient Learning and Auction Design
Miro Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Proceedings of the 58th Annual Symposium on Foundations of Computer Science (FOCS), 2017. | October 2017
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The Double Clinching Auction for Wagering
Rupert Freeman, David Pennock, Jennifer Wortman Vaughan
EC'17, Cambridge, Massachusetts, USA | June 2017
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A Decomposition of Forecast Error in Prediction Markets
Miro Dudík, Sébastien Lahaie, Ryan Rogers, Jennifer Wortman Vaughan
31st Conference on Neural Information Processing Systems (NIPS 2017) | February 2017
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The Communication Network Within the Crowd
Ming Yin, Mary L. Gray, Siddharth Suri, Jennifer Wortman Vaughan
25th International World Wide Web Conference (WWW) 2016 | April 2016
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The Possibilities and Limitations of Private Prediction Markets
Rachel Cummings, David Pennock, Jennifer Wortman Vaughan
17th ACM Conference on Economics and Computation (EC 2016) | April 2016
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Bounded Rationality in Wagering Mechanisms
David Pennock, Vasilis Syrgkanis, Jennifer Wortman Vaughan
32nd Conference on Uncertainty in Artificial Intelligence | January 2016
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Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets
Hoda Heidari, Sébastien Lahaie, David Pennock, Jennifer Wortman Vaughan
June 2015
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Incentivizing High Quality Crowdwork
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan
Twenty-Fourth International World Wide Web Conference | May 2015
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Market Making with Decreasing Utility for Information
Miroslav Dudík, Rafael Frongillo, Jennifer Wortman Vaughan
30th Conference on Uncertainty in Artificial Intelligence | July 2014
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Removing Arbitrage from Wagering Mechanisms
Yiling Chen, Nikhil Devanur, David Pennock, Jennifer Wortman Vaughan
Fifteenth ACM Conference on Economics and Computation | June 2014
-
A General Volume-Parameterized Market Making Framework
Jacob Abernethy, Rafael Frongillo, Xiaolong Li, Jennifer Wortman Vaughan
ACM Conference on Electronic Commerce | January 2014
-
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
15th ACM Conf. on Economics and Computation (EC) | January 2014
-
Adaptive Task Assignment for Crowdsourced Classification
Chien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan
30th International Conference on Machine Learning (ICML) | May 2013
-
An Axiomatic Characterization of Adaptive-Liquidity Market Makers
Xiaolong Li, Jennifer Wortman Vaughan
Fourteenth ACM Conference on Electronic Commerce (EC) | May 2013
-
Cost Function Market Makers for Measurable Spaces
Yiling Chen, Michael Ruberry, Jennifer Wortman Vaughan
Fourteenth ACM Conference on Electronic Commerce (EC) | January 2013
-
Online Task Assignment in Crowdsourcing Markets
Chien-Ju Ho, Jennifer Wortman Vaughan
Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI) | January 2012
-
Designing Informative Securities
Yiling Chen, Mike Ruberry, Jennifer Wortman Vaughan
28th Conference on Uncertainty in Artificial Intelligence (UAI) | January 2012
-
An Optimization-Based Framework for Automated Market-Making
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Twelfth ACM Conference on Electronic Commerce (EC) | January 2011
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Position: To Make Text-to-Image Models that Work for Marginalized Communities, We Need New Measurement Practices for the Long Tail
Nari Johnson, Hamna ., Deepthi Sudharsan, Theo Holroyd, Samantha Dalal, Siobhan Mackenzie Hall, Jennifer Wortman Vaughan, Daniela Massiceti, Cecily Morrison
July 2025
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Dimensions of Generative AI Evaluation Design
Alex Dow, Jennifer Wortman Vaughan, Solon Barocas, Chad Atalla, Alex Chouldechova, Hanna Wallach
November 2024
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Evaluating Generative AI Systems is a Social Science Measurement Challenge
Hanna Wallach, Meera Desai, Nick Pangakis, A. Feder Cooper, Angelina Wang, Solon Barocas, Alex Chouldechova, Chad Atalla, Su Lin Blodgett, Emily Corvi, Alex Dow, Jean Garcia-Gathright, Alexandra Olteanu, Stefanie Reed, Emily Sheng, Dan Vann, Jennifer Wortman Vaughan, Matthew Vogel, Hannah Washington, Abigail Z. Jacobs
November 2024
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The CLeAR Documentation Framework for AI Transparency: Recommendations for Practitioners & Context for Policymakers
Kasia Chmielinski, Sarah Newman, Chris N. Kranzinger, Michael Hind, Jennifer Wortman Vaughan, Margaret Mitchell, Julia Stoyanovich, Angelina McMillan-Major, Emily McReynolds, Kathleen Esfahany, Mary L. Gray, Audrey Chang, Maui Hudson
April 2024
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A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition
Forough Poursabzi-Sangdeh, Samira Samadi, Jennifer Wortman Vaughan, Hanna Wallach
April 2020
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Microsoft New Future of Work Report 2024
Jenna Butler, Mihaela Vorvoreanu, Rebecca Janssen, Abigail Sellen, Nicole Immorlica, Adam Troy, Advait Sarkar, Alex Farach, Alex Chouldechova, Alexandra Olteanu, Alexia Cambon, Arjun Radhakrishna, Asta Roseway, Ben Zorn, Brent Hecht, Daniel G. Goldstein, Dhruv Joshi, Ed Cutrell, Emre Kiciman, Gonzalo Ramos, Gustavo Soares, Hanna Wallach, Ian Drosos, Jack Williams (johnwilliams), Jacki O'Neill, Jake Hofman, Jaime Teevan, Javier Hernandez, Jennifer Wortman Vaughan, Jina Suh, John Tang, Justin Edwards, Kalika Bali, Kori Inkpen, Krishna Madhavan, Laylah Bulman, Leon Reicherts, Lev Tankelevitch, Longqi Yang, Martez Mott, Millicent Ochieng, Mercy Muchai, Nancy Baym, Najeeb Abdulhamid, Nicolai Marquardt, Ken Hinckley, Michael Bentley, Dave Brown, Hugo Romat, Nathalie Henry Riche, Samuel Maina, Shamsi Iqbal, Siân Lindley, Stephanie Nyairo, Su Lin Blodgett, Sumit Gulwani, Sunayana Sitaram, Vu Le
MSR-TR-2024-56 | December 2024
글쓴이 Microsoft
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Challenges in Human-Agent Communication
Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi, Eric Horvitz, Adam Fourney, Hussein Mozannar, Victor Dibia, Daniel S. Weld
MSR-TR-2024-53 | December 2024
글쓴이 Microsoft
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Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, Jennifer Wortman Vaughan
ToCHI | April 2025, 제 32 권(1)
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AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap
Q. Vera Liao, Jennifer Wortman Vaughan
Harvard Data Science Review | February 2024
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Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
Proc. ACM Hum.-Comput. Interact. | October 2023, 제 7 권
Article 370
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Summarize with Caution: Comparing Global Feature Attributions
Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna Wallach, Jennifer Wortman Vaughan
Bulletin of the IEEE Computer Society Technical Committee on Data Engineering | December 2021
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Datasheets for Datasets
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford
Communications of the ACM | December 2021, 제 64 권(12): pp. 86-92
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Responsible computing during COVID-19 and beyond
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miro Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, Marion Zepf
Communications of the ACM | July 2021, 제 67 권(7): pp. 30-32
https://doi.org/10.1145/3466612
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Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets
Hoda Heidari, Sebastien Lahaie, David Pennock, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation (TEAC) - Special Issue on EC'15 | November 2018, 제 6 권(3–4)
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Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
JMLR | September 2018, 제 18 권(193): pp. 1-46
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Mathematical Foundations for Social Computing
Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, Jennifer Wortman Vaughan
Communications of the ACM | December 2016, 제 59 권(12): pp. 102-108
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Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Journal of Artificial Intelligence Research | February 2016, 제 55 권
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Belief Aggregation with Automated Market Makers
Rajiv Sethi, Jennifer Wortman Vaughan
Computational Economics | June 2015
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An Axiomatic Characterization of Wagering Mechanisms
Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, David Pennock, Nicolas S. Lambert, John Langford, Yoav Shoham
Journal of Economic Theory | March 2014, 제 156 권: pp. 389-416
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Online Decision Making in Crowdsourcing Markets: Theoretical Challenges
Aleksandrs Slivkins, Jennifer Wortman Vaughan
ACM SIGecom Exchanges | December 2013, 제 12 권(2): pp. 4-23
Position paper and survey.
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Computational social science and social computing
Winter Mason, Jennifer Wortman Vaughan, Hanna Wallach
Mach Learn | November 2013, 제 95 권: pp. 257-260
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Efficient Market Making via Convex Optimization, and a Connection to Online Learning
Jacob Abernethy, Yiling Chen, Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation | May 2013, 제 1 권
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A Human-Centered Agenda for Intelligible Machine Learning
Jennifer Wortman Vaughan, Hanna Wallach
In Machines We Trust: Perspectives on Dependable AI | 글쓴이 MIT Press | 2021
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