Jennifer Wortman Vaughan

ORCID

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 82

Top Venues

CoRR

CHI

Proc. ACM Hum. Comput. Interact.

Publications

Nina Grgic-Hlaca, Junaid Ali, Krishna P. Gummadi, Jennifer Wortman Vaughan
(De)Noise: Moderating the Inconsistency Between Human Decision-Makers. CoRR (2024)
K. J. Kevin Feng, Q. Vera Liao, Ziang Xiao, Jennifer Wortman Vaughan, Amy X. Zhang, David W. McDonald
Canvil: Designerly Adaptation for LLM-Powered User Experiences. CoRR (2024)
Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, Jennifer Wortman Vaughan
"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust. FAccT (2024)
Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, Jennifer Wortman Vaughan
"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust. CoRR (2024)
Michael A. Madaio, Jingya Chen, Hanna M. Wallach, Jennifer Wortman Vaughan
Tinker, Tailor, Configure, Customize: The Articulation Work of Contextualizing an AI Fairness Checklist. Proc. ACM Hum. Comput. Interact. 8 (CSCW1) (2024)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits. SIAM J. Comput. 52 (2) (2023)
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, Jennifer Wortman Vaughan
Generation Probabilities Are Not Enough: Exploring the Effectiveness of Uncertainty Highlighting in AI-Powered Code Completions. CoRR (2023)
Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan
Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment. CoRR (2023)
Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience. CoRR (2023)
Anthony Cintron Roman, Jennifer Wortman Vaughan, Valerie See, Steph Ballard, Nicolas Schifano, Jehu Torres Vega, Caleb Robinson, Juan M. Lavista Ferres
Open Datasheets: Machine-readable Documentation for Open Datasets and Responsible AI Assessments. CoRR (2023)
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. CoRR (2023)
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, Duen Horng Chau
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse. CHI (2023)
Q. Vera Liao, Jennifer Wortman Vaughan
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap. CoRR (2023)
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David M. Pennock, Andreas Krause
Incentive-Compatible Forecasting Competitions. Manag. Sci. 69 (3) (2023)
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations. CoRR (2023)
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, Gagan Bansal
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations. Proc. ACM Hum. Comput. Interact. 7 (CSCW2) (2023)
Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, Jennifer Wortman Vaughan
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience. CHI (2023)
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. CoRR (2022)
Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna M. Wallach, Jennifer Wortman Vaughan
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research. FAccT (2022)
Amy Heger, Elizabeth B. Marquis, Mihaela Vorvoreanu, Hanna M. Wallach, Jennifer Wortman Vaughan
Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata. CoRR (2022)
Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna M. Wallach, Jennifer Wortman Vaughan
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research. CoRR (2022)
Neha Hulkund, Nicolò Fusi, Jennifer Wortman Vaughan, David Alvarez-Melis
Interpretable Distribution Shift Detection using Optimal Transport. CoRR (2022)
Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson, Nihar B. Shah
How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions? CoRR (2022)
Amy Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna M. Wallach, Jennifer Wortman Vaughan
Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata. Proc. ACM Hum. Comput. Interact. 6 (CSCW2) (2022)
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark E. Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values. KDD (2022)
Amy Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna M. Wallach, Jennifer Wortman Vaughan
Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata. Proc. ACM Hum. Comput. Interact. 6 (CSCW2) (2022)
Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, Hanna M. Wallach
Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support. Proc. ACM Hum. Comput. Interact. 6 (CSCW1) (2022)
David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna M. Wallach, Jennifer Wortman Vaughan
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence. HCOMP (2021)
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford
Datasheets for datasets. Commun. ACM 64 (12) (2021)
Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, Hanna M. Wallach
Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support. CoRR (2021)
David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna M. Wallach, Jennifer Wortman Vaughan
A Human-Centered Interpretability Framework Based on Weight of Evidence. CoRR (2021)
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna M. Wallach
Manipulating and Measuring Model Interpretability. CHI (2021)
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, Rich Caruana
GAM Changer: Editing Generalized Additive Models with Interactive Visualization. CoRR (2021)
Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna M. Wallach, Jennifer Wortman Vaughan
Summarize with Caution: Comparing Global Feature Attributions. IEEE Data Eng. Bull. 44 (4) (2021)
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, W. Duncan Wadsworth, Hanna M. Wallach
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs. AIES (2021)
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David M. Pennock, Andreas Krause
Incentive-Compatible Forecasting Competitions. CoRR (2021)
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miroslav Dudík, Benjamin Fish, Mary L. Gray, Brent J. Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna M. Wallach, Marion Zepf
Responsible computing during COVID-19 and beyond. Commun. ACM 64 (7) (2021)
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, W. Duncan Wadsworth, Hanna M. Wallach
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs. CoRR (2021)
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. DaSH@KDD (2021)
Rupert Freeman, David M. Pennock, Dominik Peters, Jennifer Wortman Vaughan
Truthful aggregation of budget proposals. J. Econ. Theory 193 (2021)
Miroslav Dudíte, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Oracle-efficient Online Learning and Auction Design. J. ACM 67 (5) (2020)
Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna M. Wallach, Meredith Ringel Morris
Toward fairness in AI for people with disabilities SBG@a research roadmap. ACM SIGACCESS Access. Comput. 125 (2020)
Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan
No-Regret and Incentive-Compatible Online Learning. CoRR (2020)
Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, Jennifer Wortman Vaughan
Mathematical Foundations for Social Computing. CoRR (2020)
Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan
The Possibilities and Limitations of Private Prediction Markets. ACM Trans. Economics and Comput. 8 (3) (2020)
Michael A. Madaio, Luke Stark, Jennifer Wortman Vaughan, Hanna M. Wallach
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI. CHI (2020)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits. CoRR (2020)
Rupert Freeman, David M. Pennock, Chara Podimata, Jennifer Wortman Vaughan
No-Regret and Incentive-Compatible Online Learning. ICML (2020)
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna M. Wallach, Jennifer Wortman Vaughan
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning. CHI (2020)
Vincent Conitzer, Rupert Freeman, Nisarg Shah, Jennifer Wortman Vaughan
Group Fairness for the Allocation of Indivisible Goods. AAAI (2019)
Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna M. Wallach, Meredith Ringel Morris
Toward Fairness in AI for People with Disabilities: A Research Roadmap. CoRR (2019)
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
The Disparate Effects of Strategic Manipulation. FAT (2019)
Rupert Freeman, David M. Pennock, Dominik Peters, Jennifer Wortman Vaughan
Truthful Aggregation of Budget Proposals. CoRR (2019)
David Alvarez-Melis, Hal Daumé III, Jennifer Wortman Vaughan, Hanna M. Wallach
Weight of Evidence as a Basis for Human-Oriented Explanations. CoRR (2019)
Rupert Freeman, David M. Pennock, Jennifer Wortman Vaughan
An Equivalence between Wagering and Fair-Division Mechanisms. AAAI (2019)
Rediet Abebe, Shawndra Hill, Jennifer Wortman Vaughan, Peter M. Small, H. Andrew Schwartz
Using Search Queries to Understand Health Information Needs in Africa. ICWSM (2019)
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna M. Wallach
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? CHI (2019)
Ming Yin, Jennifer Wortman Vaughan, Hanna M. Wallach
Understanding the Effect of Accuracy on Trust in Machine Learning Models. CHI (2019)
Rupert Freeman, David M. Pennock, Dominik Peters, Jennifer Wortman Vaughan
Truthful Aggregation of Budget Proposals. EC (2019)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
The Externalities of Exploration and How Data Diversity Helps Exploitation. CoRR (2018)
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna M. Wallach, Hal Daumé III, Kate Crawford
Datasheets for Datasets. CoRR (2018)
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, Hanna M. Wallach
Manipulating and Measuring Model Interpretability. CoRR (2018)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, Zhiwei Steven Wu
The Externalities of Exploration and How Data Diversity Helps Exploitation. COLT (2018)
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David M. Pennock, Andreas Krause
Incentive-Compatible Forecasting Competitions. AAAI (2018)
Hoda Heidari, Sébastien Lahaie, David M. Pennock, Jennifer Wortman Vaughan
Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets. ACM Trans. Economics and Comput. 6 (3-4) (2018)
Rediet Abebe, Shawndra Hill, Jennifer Wortman Vaughan, Peter M. Small, H. Andrew Schwartz
Using Search Queries to Understand Health Information Needs in Africa. CoRR (2018)
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, Hanna M. Wallach
Improving fairness in machine learning systems: What do industry practitioners need? CoRR (2018)
Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
The Disparate Effects of Strategic Manipulation. CoRR (2018)
Miroslav Dudík, Sébastien Lahaie, Ryan M. Rogers, Jennifer Wortman Vaughan
A Decomposition of Forecast Error in Prediction Markets. CoRR (2017)
Jennifer Wortman Vaughan
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research. J. Mach. Learn. Res. 18 (2017)
Jennifer Wortman Vaughan
Incentives and the crowd. XRDS 24 (1) (2017)
Jennifer Wortman Vaughan
Tutorial: Making Better Use of the Crowd. ACL (Tutorial Abstracts) (2017)
Miroslav Dudík, Sébastien Lahaie, Ryan M. Rogers, Jennifer Wortman Vaughan
A Decomposition of Forecast Error in Prediction Markets. NIPS (2017)
Rupert Freeman, David M. Pennock, Jennifer Wortman Vaughan
The Double Clinching Auction for Wagering. EC (2017)
Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Oracle-Efficient Online Learning and Auction Design. FOCS (2017)
Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan
The Possibilities and Limitations of Private Prediction Markets. CoRR (2016)
Ming Yin, Mary L. Gray, Siddharth Suri, Jennifer Wortman Vaughan
The Communication Network Within the Crowd. WWW (2016)
Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Oracle-Efficient Learning and Auction Design. CoRR (2016)
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems. J. Artif. Intell. Res. 55 (2016)
Rachel Cummings, David M. Pennock, Jennifer Wortman Vaughan
The Possibilities and Limitations of Private Prediction Markets. EC (2016)
Yiling Chen, Arpita Ghosh, Michael J. Kearns, Tim Roughgarden, Jennifer Wortman Vaughan
Mathematical foundations for social computing. Commun. ACM 59 (12) (2016)
David M. Pennock, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Bounded Rationality in Wagering Mechanisms. UAI (2016)
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan
Incentivizing High Quality Crowdwork. CoRR (2015)
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan
Incentivizing high quality crowdwork. SIGecom Exch. 14 (2) (2015)
Nicolas S. Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel M. Reeves, Yoav Shoham, David M. Pennock
An axiomatic characterization of wagering mechanisms. J. Econ. Theory 156 (2015)
Hoda Heidari, Sébastien Lahaie, David M. Pennock, Jennifer Wortman Vaughan
Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets. EC (2015)
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, Jennifer Wortman Vaughan
Incentivizing High Quality Crowdwork. WWW (2015)
Winter A. Mason, Jennifer Wortman Vaughan, Hanna M. Wallach
Computational social science and social computing. Mach. Learn. 95 (3) (2014)
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems. CoRR (2014)
Yiling Chen, Nikhil R. Devanur, David M. Pennock, Jennifer Wortman Vaughan
Removing arbitrage from wagering mechanisms. EC (2014)
Jacob D. Abernethy, Rafael M. Frongillo, Xiaolong Li, Jennifer Wortman Vaughan
A general volume-parameterized market making framework. EC (2014)
Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Adaptive contract design for crowdsourcing markets: bandit algorithms for repeated principal-agent problems. EC (2014)
Miroslav Dudík, Rafael M. Frongillo, Jennifer Wortman Vaughan
Market Making with Decreasing Utility for Information. UAI (2014)
Miroslav Dudík, Rafael M. Frongillo, Jennifer Wortman Vaughan
Market Making with Decreasing Utility for Information. CoRR (2014)
Aleksandrs Slivkins, Jennifer Wortman Vaughan
Online decision making in crowdsourcing markets: theoretical challenges. SIGecom Exch. 12 (2) (2013)
Chien-Ju Ho, Shahin Jabbari, Jennifer Wortman Vaughan
Adaptive Task Assignment for Crowdsourced Classification. ICML (1) (2013)
Xiaolong Li, Jennifer Wortman Vaughan
An axiomatic characterization of adaptive-liquidity market makers. EC (2013)
Yiling Chen, Mike Ruberry, Jennifer Wortman Vaughan
Cost function market makers for measurable spaces. EC (2013)
Jacob D. Abernethy, Yiling Chen, Jennifer Wortman Vaughan
Efficient Market Making via Convex Optimization, and a Connection to Online Learning. ACM Trans. Economics and Comput. 1 (2) (2013)
Aleksandrs Slivkins, Jennifer Wortman Vaughan
Online Decision Making in Crowdsourcing Markets: Theoretical Challenges (Position Paper). CoRR (2013)

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Publication Activity (10 Years)

Top Topics

Top Venues

Jennifer Wortman Vaughan