Transparency for Generative AI
Challenges in Human-Agent Communication (preprint)
Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi, Eric Horvitz, Adam Fourney, Hussein Mozannar, Victor Dibia, and Daniel S. Weld
Working paper, 2024
Gagan Bansal, Jennifer Wortman Vaughan, Saleema Amershi, Eric Horvitz, Adam Fourney, Hussein Mozannar, Victor Dibia, and Daniel S. Weld
Working paper, 2024
Generation Probabilities Are Not Enough: Exploring the Effectiveness of Uncertainty Highlighting in AI-Powered Code Completions
(arxiv preprint)
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, and Jennifer Wortman Vaughan
ACM Transactions on Computer-Human Interaction, to appear 2024
(A preliminary version appeared at the NeurIPS 2022 Human-Centered AI workshop)
Helena Vasconcelos, Gagan Bansal, Adam Fourney, Q. Vera Liao, and Jennifer Wortman Vaughan
ACM Transactions on Computer-Human Interaction, to appear 2024
(A preliminary version appeared at the NeurIPS 2022 Human-Centered AI workshop)
"I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust (PDF)
Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, and Jennifer Wortman Vaughan
In the 7th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2024)
Sunnie S. Y. Kim, Q. Vera Liao, Mihaela Vorvoreanu, Stephanie Ballard, and Jennifer Wortman Vaughan
In the 7th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2024)
AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap (arxiv preprint, publisher's page)
Q. Vera Liao and Jennifer Wortman Vaughan
In Harvard Data Science Review, Special Issue 5, 2024
Q. Vera Liao and Jennifer Wortman Vaughan
In Harvard Data Science Review, Special Issue 5, 2024
Human-Centered Approaches to Transparency
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations
(PDF)
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, and Gagan Bansal
In the 26th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2023)
Valerie Chen, Q. Vera Liao, Jennifer Wortman Vaughan, and Gagan Bansal
In the 26th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2023)
GAM Coach: Towards Interactive and User-Centered Algorithmic Recourse (PDF, website)
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, and Duen Horng Chau
In the 2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023)
Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana, and Duen Horng Chau
In the 2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023)
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values (PDF, website)
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, and Rich Caruana
In the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022)
(Preliminary version received a Best Paper Award at the NeurIPS 2021 Workshop on Bridging the Gap: From Machine Learning Research to Clinical Practice)
Zijie J. Wang, Alex Kale, Harsha Nori, Peter Stella, Mark Nunnally, Duen Horng Chau, Mihaela Vorvoreanu, Jennifer Wortman Vaughan, and Rich Caruana
In the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022)
(Preliminary version received a Best Paper Award at the NeurIPS 2021 Workshop on Bridging the Gap: From Machine Learning Research to Clinical Practice)
Summarize with Caution: Comparing Global Feature Attributions (PDF)
Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna Wallach, and Jennifer Wortman Vaughan
IEEE Data Engineering Bulletin, December 2021
Alex Okeson, Rich Caruana, Nick Craswell, Kori Inkpen, Scott M. Lundberg, Harsha Nori, Hanna Wallach, and Jennifer Wortman Vaughan
IEEE Data Engineering Bulletin, December 2021
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence (PDF)
David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna Wallach, and Jennifer Wortman Vaughan
In the 9th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2021)
(Preliminary versions were presented at the NeurIPS 2019 Human-Centric Machine Learning Workshop and the CHI 2021 Workshop on Operationalizing Human-Centered Perspectives in Explainable AI)
David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna Wallach, and Jennifer Wortman Vaughan
In the 9th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2021)
(Preliminary versions were presented at the NeurIPS 2019 Human-Centric Machine Learning Workshop and the CHI 2021 Workshop on Operationalizing Human-Centered Perspectives in Explainable AI)
A Human-Centered Agenda for Intelligible Machine Learning (preprint)
Jennifer Wortman Vaughan and Hanna Wallach
In Machines We Trust: Perspectives on Dependable AI, edited by Marcello Pelillo and Teresa Scantamburlo, MIT Press, 2021.
Jennifer Wortman Vaughan and Hanna Wallach
In Machines We Trust: Perspectives on Dependable AI, edited by Marcello Pelillo and Teresa Scantamburlo, MIT Press, 2021.
Manipulating and Measuring Model Interpretability (PDF)
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach
In the 2021 ACM CHI Conference on Human Factors in Computing Systems (CHI 2021)
(Preliminary versions were presented at the NeurIPS 2017 Interpretable Machine Learning Symposium, the NeurIPS 2017 Workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments, and IC2S2 2019)
Forough Poursabzi-Sangdeh, Daniel G. Goldstein, Jake M. Hofman, Jennifer Wortman Vaughan, and Hanna Wallach
In the 2021 ACM CHI Conference on Human Factors in Computing Systems (CHI 2021)
(Preliminary versions were presented at the NeurIPS 2017 Interpretable Machine Learning Symposium, the NeurIPS 2017 Workshop on Transparent and Interpretable Machine Learning in Safety Critical Environments, and IC2S2 2019)
Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning (PDF)
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan.
In the 2020 ACM CHI Conference on Human Factors in Computing Systems (CHI 2020)
Recipient of an Honorable Mention Award at CHI 2020
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan.
In the 2020 ACM CHI Conference on Human Factors in Computing Systems (CHI 2020)
Recipient of an Honorable Mention Award at CHI 2020
Understanding the Effect of Accuracy on Trust in Machine Learning Models (PDF)
Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach
In the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019)
(A preliminary version appeared in the ICML/IJCAI 2018 Workshop on Human Interpretability in Machine Learning)
Recipient of an Honorable Mention Award at CHI 2019
Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach
In the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019)
(A preliminary version appeared in the ICML/IJCAI 2018 Workshop on Human Interpretability in Machine Learning)
Recipient of an Honorable Mention Award at CHI 2019
Other AI-Assisted Decision Making
(De)Noise: Moderating the Inconsistency Between Human Decision-Makers (arxiv preprint)
Nina Grgić-Hlača, Junaid Ali, Krishna P. Gummadi, and Jennifer Wortman Vaughan
In the 27th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2024)
Nina Grgić-Hlača, Junaid Ali, Krishna P. Gummadi, and Jennifer Wortman Vaughan
In the 27th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2024)
Beyond End Predictions: Stop Putting Machine Learning First and Design Human-Centered AI for Decision Support (PDF)
Zana Buçinca, Alexandra Chouldechova, Jennifer Wortman Vaughan, and Krzysztof Z. Gajos
Short position paper in the NeurIPS 2022 Human-Centered AI Workshop
Zana Buçinca, Alexandra Chouldechova, Jennifer Wortman Vaughan, and Krzysztof Z. Gajos
Short position paper in the NeurIPS 2022 Human-Centered AI Workshop
Responsible Research Practices
Supporting Industry Computing Researchers in Assessing, Articulating, and Addressing the Potential Negative Societal Impact of Their Work (arxiv preprint)
Wesley Hanwen Deng, Solon Barocas, and Jennifer Wortman Vaughan
To appear in the 28th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2025)
Wesley Hanwen Deng, Solon Barocas, and Jennifer Wortman Vaughan
To appear in the 28th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2025)
REAL ML: Recognizing, Exploring, and Articulating Limitations in Machine Learning Research (PDF, tool)
Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, and Jennifer Wortman Vaughan
In the 5th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2022)
Jessie J. Smith, Saleema Amershi, Solon Barocas, Hanna Wallach, and Jennifer Wortman Vaughan
In the 5th ACM Conference on Fairness, Accountability, and Transparency (FAccT 2022)
Data and Model Documentation
The CLeAR Documentation Framework for AI Transparency: Recommendations for Practitioners & Context for Policymakers (link)
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, and Maui Hudson
Harvard Kennedy School Shorenstein Center discussion paper, April 2024
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, and Maui Hudson
Harvard Kennedy School Shorenstein Center discussion paper, April 2024
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience (PDF)
Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, and Jennifer Wortman Vaughan
In the 2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023)
Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, and Jennifer Wortman Vaughan
In the 2023 ACM CHI Conference on Human Factors in Computing Systems (CHI 2023)
Understanding Machine Learning Practitioners' Data Documentation Perceptions, Needs, Challenges, and Desiderata (PDF, project page)
Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, and Jennifer Wortman Vaughan
In the 25th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2022)
Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, and Jennifer Wortman Vaughan
In the 25th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2022)
Datasheets for Datasets (full text at CACM,
arxiv)
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford
Communications of the ACM, Volume 64, Number 12, pages 86-92, December 2021 (Short version appeared at FATML 2018)
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford
Communications of the ACM, Volume 64, Number 12, pages 86-92, December 2021 (Short version appeared at FATML 2018)
Fairness in Machine Learning
Tinker, Tailor, Configure, Customize: The Articulation Work of Contextualizing AI Fairness Checklists (PDF)
Michael Madaio, Jingya Chen, Hanna Wallach, and Jennifer Wortman Vaughan
In the 27th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2024)
Michael Madaio, Jingya Chen, Hanna Wallach, and Jennifer Wortman Vaughan
In the 27th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2024)
Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support (PDF)
Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, and Hanna Wallach
In the 25th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2022)
Michael Madaio, Lisa Egede, Hariharan Subramonyam, Jennifer Wortman Vaughan, and Hanna Wallach
In the 25th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2022)
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
(PDF)
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, Duncan Wadsworth, and Hanna Wallach
In the Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES 2021)
Solon Barocas, Anhong Guo, Ece Kamar, Jacquelyn Krones, Meredith Ringel Morris, Jennifer Wortman Vaughan, Duncan Wadsworth, and Hanna Wallach
In the Fourth AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES 2021)
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI (PDF)
Michael Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach
In the 2020 ACM CHI Conference on Human Factors in Computing Systems (CHI 2020)
Recipient of a Best Paper Award at CHI 2020
In the 2020 ACM CHI Conference on Human Factors in Computing Systems (CHI 2020)
Recipient of a Best Paper Award at CHI 2020
A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition (PDF)
Forough Poursabzi-Sangdeh, Samira Samadi, Jennifer Wortman Vaughan, and Hanna Wallach
CHI Workshop on Human-Centered Approaches to Fair and Responsible AI, 2020
Forough Poursabzi-Sangdeh, Samira Samadi, Jennifer Wortman Vaughan, and Hanna Wallach
CHI Workshop on Human-Centered Approaches to Fair and Responsible AI, 2020
Toward Fairness in AI for People with Disabilities: A Research Roadmap (html version or PDF on arxiv)
Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna Wallach, and Meredith Ringel Morris
ACM SIGACCESS Newsletter, 125, October 2019
(Also appeared in the ASSETS 2019 Workshop on AI Fairness for People with Disabilities)
Anhong Guo, Ece Kamar, Jennifer Wortman Vaughan, Hanna Wallach, and Meredith Ringel Morris
ACM SIGACCESS Newsletter, 125, October 2019
(Also appeared in the ASSETS 2019 Workshop on AI Fairness for People with Disabilities)
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (PDF)
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, and Hanna Wallach
In the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019)
(A preliminary version appeared in the NeurIPS 2018 Workshop on Critiquing and Correcting Trends in ML)
Kenneth Holstein, Jennifer Wortman Vaughan, Hal Daumé III, Miroslav Dudík, and Hanna Wallach
In the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019)
(A preliminary version appeared in the NeurIPS 2018 Workshop on Critiquing and Correcting Trends in ML)
The Disparate Effects of Strategic Manipulation (long version on arxiv)
Lily Hu, Nicole Immorlica, and Jennifer Wortman Vaughan
In the 2nd ACM Conference on Fairness, Accountability, and Transparency (FAccT 2019)
Lily Hu, Nicole Immorlica, and Jennifer Wortman Vaughan
In the 2nd ACM Conference on Fairness, Accountability, and Transparency (FAccT 2019)
The Externalities of Exploration and How Data Diversity Helps Exploitation (long version on arxiv)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu
In the 31st Annual Conference on Learning Theory (COLT 2018)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu
In the 31st Annual Conference on Learning Theory (COLT 2018)
Other Fair Decision Making
An Equivalence Between Fair Division and Wagering Mechanisms (Publisher's Page)
Rupert Freeman, Jens Witkowski, Jennifer Wortman Vaughan, and David M. Pennock
Management Science, Volume 70, Number 10, pages 6704-6723, 2024
(Supersedes the AAAI 2019 paper)
Rupert Freeman, Jens Witkowski, Jennifer Wortman Vaughan, and David M. Pennock
Management Science, Volume 70, Number 10, pages 6704-6723, 2024
(Supersedes the AAAI 2019 paper)
Truthful Aggregation of Budget Proposals (preprint)
Rupert Freeman, David Pennock, Dominik Peters, and Jennifer Wortman Vaughan
Journal of Economic Theory, Volume 193, 2021
(Supersedes the EC 2019 paper)
Rupert Freeman, David Pennock, Dominik Peters, and Jennifer Wortman Vaughan
Journal of Economic Theory, Volume 193, 2021
(Supersedes the EC 2019 paper)
Truthful Aggregation of Budget Proposals (long version on arxiv)
Rupert Freeman, David Pennock, Dominik Peters, and Jennifer Wortman Vaughan
In the Twentieth ACM Conference on Economics and Computation (EC 2019)
(A short version also appeared in the AAMAS 2019 Workshop on Fair Allocation in Multiagent Systems)
Rupert Freeman, David Pennock, Dominik Peters, and Jennifer Wortman Vaughan
In the Twentieth ACM Conference on Economics and Computation (EC 2019)
(A short version also appeared in the AAMAS 2019 Workshop on Fair Allocation in Multiagent Systems)
Group Fairness for Indivisible Goods Allocation (long version)
Vincent Conitzer, Rupert Freeman, Nisarg Shah, and Jennifer Wortman Vaughan
In the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019)
(A short version also appeared in the EC 2019 Workshop on Mechanism Design for Social Good)
Vincent Conitzer, Rupert Freeman, Nisarg Shah, and Jennifer Wortman Vaughan
In the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019)
(A short version also appeared in the EC 2019 Workshop on Mechanism Design for Social Good)
An Equivalence Between Wagering and Fair-Division Mechanisms (long version)
Rupert Freeman, David Pennock, and Jennifer Wortman Vaughan
In the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019)
Rupert Freeman, David Pennock, and Jennifer Wortman Vaughan
In the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019)
Peer Review
How do Authors’ Perceptions of their Papers Compare with Co-authors’ Perceptions and Peer-review Decisions? (long version on arxiv, NeurIPS blog post)
Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson, and Nihar B. Shah
Report from NeurIPS 2021
Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson, and Nihar B. Shah
Report from NeurIPS 2021
Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment (long version on arxiv, NeurIPS blog post)
Alina Beygelzimer, Yann Dauphin, Percy Liang, and Jennifer Wortman Vaughan
Report from NeurIPS 2021
Alina Beygelzimer, Yann Dauphin, Percy Liang, and Jennifer Wortman Vaughan
Report from NeurIPS 2021
Crowdsourcing and Human Computation
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research
(Publisher's Page)
Jennifer Wortman Vaughan
Journal of Machine Learning Research, Volume 18, Number 193, Pages 1-46, 2018
Jennifer Wortman Vaughan
Journal of Machine Learning Research, Volume 18, Number 193, Pages 1-46, 2018
Mathematical Foundations for Social Computing
(full text at CACM, related CCC white paper)
Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, and Jennifer Wortman Vaughan
Communications of the ACM, Volume 59, Number 12, pages 102-108, December 2016
Yiling Chen, Arpita Ghosh, Michael Kearns, Tim Roughgarden, and Jennifer Wortman Vaughan
Communications of the ACM, Volume 59, Number 12, pages 102-108, December 2016
The Communication Network Within the Crowd
(PDF)
Ming Yin, Mary Gray, Siddharth Suri, and Jennifer Wortman Vaughan
In the Twenty-Fifth International World Wide Web Conference (WWW 2016)
Ming Yin, Mary Gray, Siddharth Suri, and Jennifer Wortman Vaughan
In the Twenty-Fifth International World Wide Web Conference (WWW 2016)
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
(Publisher's Page)
Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan
Journal of Artificial Intelligence Research, Volume 55, Pages 317-359, 2016
(Supersedes the EC 14 paper)
Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan
Journal of Artificial Intelligence Research, Volume 55, Pages 317-359, 2016
(Supersedes the EC 14 paper)
Incentivizing High Quality Crowdwork
(PDF)
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, and Jennifer Wortman Vaughan
In the Twenty-Fourth International World Wide Web Conference (WWW 2015)
Nominee for Best Paper Award at WWW
Chien-Ju Ho, Aleksandrs Slivkins, Siddharth Suri, and Jennifer Wortman Vaughan
In the Twenty-Fourth International World Wide Web Conference (WWW 2015)
Nominee for Best Paper Award at WWW
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
(extended version)
Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan
In the Fifteenth ACM Conference on Economics and Computation (EC 2014)
Chien-Ju Ho, Aleksandrs Slivkins, and Jennifer Wortman Vaughan
In the Fifteenth ACM Conference on Economics and Computation (EC 2014)
Online Decision Making in Crowdsourcing Markets: Theoretical Challenges (Position Paper)
(PDF)
Aleksandrs Slivkins and Jennifer Wortman Vaughan
In ACM SIGecom Exchanges, Volume 12, Number 2, December 2013
Aleksandrs Slivkins and Jennifer Wortman Vaughan
In ACM SIGecom Exchanges, Volume 12, Number 2, December 2013
Adaptive Task Assignment for Crowdsourced Classification
(PDF)
Chien-Ju Ho, Shahin Jabbari, and Jennifer Wortman Vaughan
In the 30th International Conference on Machine Learning (ICML 2013)
(Simultaneously appeared in the ACM EC 3rd Workshop on Social Computing and User Generated Content)
Chien-Ju Ho, Shahin Jabbari, and Jennifer Wortman Vaughan
In the 30th International Conference on Machine Learning (ICML 2013)
(Simultaneously appeared in the ACM EC 3rd Workshop on Social Computing and User Generated Content)
Online Task Assignment in Crowdsourcing Markets (PDF)
Chien-Ju Ho and Jennifer Wortman Vaughan
In the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2012)
Chien-Ju Ho and Jennifer Wortman Vaughan
In the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2012)
Prediction Markets and Wagering Mechanisms
Incentive-Compatible Forecasting Competitions (Publisher's Page)
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David Pennock, and Andreas Krause
Management Science, Volume 69, Number 3, pages 1354-1374, 2023
(Supersedes the AAAI 2018 paper)
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David Pennock, and Andreas Krause
Management Science, Volume 69, Number 3, pages 1354-1374, 2023
(Supersedes the AAAI 2018 paper)
Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets (Publisher's Page)
Hoda Heidari, Sébastien Lahaie, David Pennock, and Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation, Volume 6, Number 3-4, Article 15, October 2018
(Supersedes the EC 15 paper)
Hoda Heidari, Sébastien Lahaie, David Pennock, and Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation, Volume 6, Number 3-4, Article 15, October 2018
(Supersedes the EC 15 paper)
Incentive-Compatible Forecasting Competitions (PDF)
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David Pennock, and Andreas Krause
In the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018)
Jens Witkowski, Rupert Freeman, Jennifer Wortman Vaughan, David Pennock, and Andreas Krause
In the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018)
A Decomposition of Forecast Error in Prediction Markets (PDF)
Miroslav Dudík, Sébastien Lahaie, Ryan Rogers, and Jennifer Wortman Vaughan
In Advances in Neural Information Processing Systems 30 (NeurIPS 2017)
Miroslav Dudík, Sébastien Lahaie, Ryan Rogers, and Jennifer Wortman Vaughan
In Advances in Neural Information Processing Systems 30 (NeurIPS 2017)
The Double Clinching Auction for Wagering (PDF)
Rupert Freeman, David M. Pennock, and Jennifer Wortman Vaughan
In the 18th ACM Conference on Economics and Computation (EC 2017)
Rupert Freeman, David M. Pennock, and Jennifer Wortman Vaughan
In the 18th ACM Conference on Economics and Computation (EC 2017)
Bounded Rationality in Wagering Mechanisms
(PDF)
David M. Pennock, Vasilis Syrgkanis, and Jennifer Wortman Vaughan
In the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)
David M. Pennock, Vasilis Syrgkanis, and Jennifer Wortman Vaughan
In the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)
The Possibilities and Limitations of Private Prediction Markets (PDF)
Rachel Cummings, David M. Pennock, and Jennifer Wortman Vaughan
In the 17th ACM Conference on Economics and Computation (EC 2016)
Rachel Cummings, David M. Pennock, and Jennifer Wortman Vaughan
In the 17th ACM Conference on Economics and Computation (EC 2016)
Belief Aggregation with Automated Market Makers (SSRN)
Rajiv Sethi and Jennifer Wortman Vaughan
Computational Economics, Volume 48, Issue 1, Pages 155-178, 2016
Rajiv Sethi and Jennifer Wortman Vaughan
Computational Economics, Volume 48, Issue 1, Pages 155-178, 2016
Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets
(PDF)
Hoda Heidari, Sébastien Lahaie, David Pennock, and Jennifer Wortman Vaughan
In the Sixteeth ACM Conference on Economics and Computation (EC 2015)
Hoda Heidari, Sébastien Lahaie, David Pennock, and Jennifer Wortman Vaughan
In the Sixteeth ACM Conference on Economics and Computation (EC 2015)
An Axiomatic Characterization of Wagering Mechanisms (preprint)
Nicolas S. Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, Yoav Shoham, and David M. Pennock
Journal of Economic Theory, Volume 156, Pages 389-416, 2015
(Mostly supersedes the EC 08 version)
Nicolas S. Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel Reeves, Yoav Shoham, and David M. Pennock
Journal of Economic Theory, Volume 156, Pages 389-416, 2015
(Mostly supersedes the EC 08 version)
Market Making with Decreasing Utility for Information (PDF)
Miroslav Dudík, Rafael Frongillo, and Jennifer Wortman Vaughan
In the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014)
Miroslav Dudík, Rafael Frongillo, and Jennifer Wortman Vaughan
In the 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014)
A General Volume-Parameterized Market Making Framework (PDF)
Jacob Abernethy, Rafael Frongillo, Xiaolong Li, and Jennifer Wortman Vaughan
In the Fifteenth ACM Conference on Economics and Computation (EC 2014)
Jacob Abernethy, Rafael Frongillo, Xiaolong Li, and Jennifer Wortman Vaughan
In the Fifteenth ACM Conference on Economics and Computation (EC 2014)
Removing Arbitrage from Wagering Mechanisms (PDF)
Yiling Chen, Nikhil R. Devanur, David Pennock, and Jennifer Wortman Vaughan
In the Fifteenth ACM Conference on Economics and Computation (EC 2014)
Yiling Chen, Nikhil R. Devanur, David Pennock, and Jennifer Wortman Vaughan
In the Fifteenth ACM Conference on Economics and Computation (EC 2014)
An Axiomatic Characterization of Adaptive-Liquidity Market Makers (PDF)
Xiaolong Li and Jennifer Wortman Vaughan
In the Fourteenth ACM Conference on Electronic Commerce (EC 2013)
(A preliminary version appeared in the ICML 2012 Workshop on Markets, Mechanisms, and Multi-Agent Models)
Xiaolong Li and Jennifer Wortman Vaughan
In the Fourteenth ACM Conference on Electronic Commerce (EC 2013)
(A preliminary version appeared in the ICML 2012 Workshop on Markets, Mechanisms, and Multi-Agent Models)
Cost Function Market Makers for Measurable Spaces (PDF)
Yiling Chen, Michael Ruberry, and Jennifer Wortman Vaughan
In the Fourteenth ACM Conference on Electronic Commerce (EC 2013)
Yiling Chen, Michael Ruberry, and Jennifer Wortman Vaughan
In the Fourteenth ACM Conference on Electronic Commerce (EC 2013)
Efficient Market Making via Convex Optimization, and a Connection to Online Learning (preprint)
Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation, Volume 1, Number 2, Article 12, May 2013
(Supersedes the EC 10 and EC 11 papers)
Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan
ACM Transactions on Economics and Computation, Volume 1, Number 2, Article 12, May 2013
(Supersedes the EC 10 and EC 11 papers)
Designing Informative Securities
(PDF)
Yiling Chen, Mike Ruberry, and Jennifer Wortman Vaughan
In the 28th Conference on Uncertainty in Artificial Intelligence (UAI 2012)
Yiling Chen, Mike Ruberry, and Jennifer Wortman Vaughan
In the 28th Conference on Uncertainty in Artificial Intelligence (UAI 2012)
An Optimization-Based Framework for Automated Market-Making (PDF)
Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan
In the Twelfth ACM Conference on Electronic Commerce (EC 2011)
(A preliminary version appeared in the NeurIPS 2010 Workshop on Computational Social Science and the Wisdom of Crowds)
Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan
In the Twelfth ACM Conference on Electronic Commerce (EC 2011)
(A preliminary version appeared in the NeurIPS 2010 Workshop on Computational Social Science and the Wisdom of Crowds)
A New Understanding of Prediction Markets Via No-Regret Learning (PDF)
Yiling Chen and Jennifer Wortman Vaughan
In the Eleventh ACM Conference on Electronic Commerce (EC 2010)
Yiling Chen and Jennifer Wortman Vaughan
In the Eleventh ACM Conference on Electronic Commerce (EC 2010)
Connections Between Markets and Learning
(PDF)
Yiling Chen and Jennifer Wortman Vaughan
In ACM SIGecom Exchanges, Volume 9, Number 1, June 2010
(Shorter synapse of the EC 2010 paper)
Yiling Chen and Jennifer Wortman Vaughan
In ACM SIGecom Exchanges, Volume 9, Number 1, June 2010
(Shorter synapse of the EC 2010 paper)
Complexity of Combinatorial Market Makers
(PDF)
Yiling Chen, Lance Fortnow, Nicolas Lambert, David Pennock, and Jennifer Wortman
In the Ninth ACM Conference on Electronic Commerce (EC 2008)
Yiling Chen, Lance Fortnow, Nicolas Lambert, David Pennock, and Jennifer Wortman
In the Ninth ACM Conference on Electronic Commerce (EC 2008)
Self-Financed Wagering Mechanisms for Forecasting
(PDF)
Nicolas Lambert, John Langford, Jennifer Wortman, Yiling Chen, Daniel Reeves, Yoav Shoham, and David Pennock
In the Ninth ACM Conference on Electronic Commerce (EC 2008)
Winner of an Outstanding Paper Award at EC
(A preliminary version appeared in the DIMACS Workshop on the Boundary Between Economic Theory and CS)
Nicolas Lambert, John Langford, Jennifer Wortman, Yiling Chen, Daniel Reeves, Yoav Shoham, and David Pennock
In the Ninth ACM Conference on Electronic Commerce (EC 2008)
Winner of an Outstanding Paper Award at EC
(A preliminary version appeared in the DIMACS Workshop on the Boundary Between Economic Theory and CS)
Other Online Learning or Active Learning
Greedy Algorithm Almost Dominates in Smoothed Contextual Bandits (Publisher's Page, arxiv preprint)
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu
SIAM Journal on Computing, Volume 52, Number 2, pages 487-524, 2023
Manish Raghavan, Aleksandrs Slivkins, Jennifer Wortman Vaughan, and Zhiwei Steven Wu
SIAM Journal on Computing, Volume 52, Number 2, pages 487-524, 2023
Oracle-Efficient Learning and Auction Design (preprint)
Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, and Jennifer Wortman Vaughan
Journal of the ACM, Volume 67, Issue 5, Article 26, pages 1–57, 2020
(Supercedes the FOCS 2017 version)
Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, and Jennifer Wortman Vaughan
Journal of the ACM, Volume 67, Issue 5, Article 26, pages 1–57, 2020
(Supercedes the FOCS 2017 version)
No-Regret and Incentive-Compatible Online Learning (PDF)
Rupert Freeman, David M. Pennock, Chara Podimata, and Jennifer Wortman Vaughan
In the 37th International Conference on Machine Learning (ICML 2020)
(A preliminary version appeared in the NeurIPS 2019 Workshop on Machine Learning with Guarantees)
Rupert Freeman, David M. Pennock, Chara Podimata, and Jennifer Wortman Vaughan
In the 37th International Conference on Machine Learning (ICML 2020)
(A preliminary version appeared in the NeurIPS 2019 Workshop on Machine Learning with Guarantees)
Oracle-Efficient Learning and Auction Design (long version on arxiv)
Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, and Jennifer Wortman Vaughan
In the 58th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2017)
Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, and Jennifer Wortman Vaughan
In the 58th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2017)
Evolution with Drifting Targets (PDF)
Varun Kanade, Leslie G. Valiant, and Jennifer Wortman Vaughan
In the 23rd Annual Conference on Learning Theory (COLT 2010)
Varun Kanade, Leslie G. Valiant, and Jennifer Wortman Vaughan
In the 23rd Annual Conference on Learning Theory (COLT 2010)
Regret Minimization with Concept Drift (PDF)
Koby Crammer, Eyal Even-Dar, Yishay Mansour, and Jennifer Wortman Vaughan
In the 23rd Annual Conference on Learning Theory (COLT 2010)
Koby Crammer, Eyal Even-Dar, Yishay Mansour, and Jennifer Wortman Vaughan
In the 23rd Annual Conference on Learning Theory (COLT 2010)
The True Sample Complexity of Active Learning (Publisher's Page)
Maria-Florina Balcan, Steve Hanneke, and Jennifer Wortman Vaughan
Machine Learning Journal (Special issue on COLT 2008), Volume 80, Numbers 2-3, Pages 111-139, 2010
(Supersedes the COLT 08 version)
Maria-Florina Balcan, Steve Hanneke, and Jennifer Wortman Vaughan
Machine Learning Journal (Special issue on COLT 2008), Volume 80, Numbers 2-3, Pages 111-139, 2010
(Supersedes the COLT 08 version)
Censored Exploration and the Dark Pool Problem
(PDF)
Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka, and Jennifer Wortman Vaughan
In the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009)
Winner of the Best Student Paper Award at UAI
(Also appeared in Communications of the ACM, Research Highlights, May 2010 (Online Issue))
Kuzman Ganchev, Michael Kearns, Yuriy Nevmyvaka, and Jennifer Wortman Vaughan
In the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009)
Winner of the Best Student Paper Award at UAI
(Also appeared in Communications of the ACM, Research Highlights, May 2010 (Online Issue))
Regret to the Best Vs. Regret to the Average (Publisher's Page)
Eyal Even-Dar, Michael Kearns, Yishay Mansour, and Jennifer Wortman
Machine Learning Journal (Special issue on COLT 2007), Volume 72, Numbers 1-2, Pages 21-37, 2008
(Supersedes the COLT 07 version)
Eyal Even-Dar, Michael Kearns, Yishay Mansour, and Jennifer Wortman
Machine Learning Journal (Special issue on COLT 2007), Volume 72, Numbers 1-2, Pages 21-37, 2008
(Supersedes the COLT 07 version)
The True Sample Complexity of Active Learning
(PDF)
Maria-Florina Balcan, Steve Hanneke, and Jennifer Wortman
In the 21st Annual Conference on Learning Theory (COLT 2008)
Winner of the Mark Fulk Best Student Paper Award at COLT
(A preliminary version appeared in the NeurIPS 2007 Workshop on Principles of Learning Problem Design)
Maria-Florina Balcan, Steve Hanneke, and Jennifer Wortman
In the 21st Annual Conference on Learning Theory (COLT 2008)
Winner of the Mark Fulk Best Student Paper Award at COLT
(A preliminary version appeared in the NeurIPS 2007 Workshop on Principles of Learning Problem Design)
Exploration Scavenging
(PDF)
John Langford, Alexander Strehl, and Jennifer Wortman
In the 25th International Conference on Machine Learning (ICML 2008)
John Langford, Alexander Strehl, and Jennifer Wortman
In the 25th International Conference on Machine Learning (ICML 2008)
Regret to the Best Vs. Regret to the Average
(PDF)
Eyal Even-Dar, Michael Kearns, Yishay Mansour, and Jennifer Wortman
In the 20th Annual Conference on Learning Theory (COLT 2007)
Winner of a Best Student Paper Award at COLT
(A preliminary version appeared in the NeurIPS 2006 Workshop on Online Trading of Exploration and Exploitation)
Eyal Even-Dar, Michael Kearns, Yishay Mansour, and Jennifer Wortman
In the 20th Annual Conference on Learning Theory (COLT 2007)
Winner of a Best Student Paper Award at COLT
(A preliminary version appeared in the NeurIPS 2006 Workshop on Online Trading of Exploration and Exploitation)
Risk-Sensitive Online Learning
(Corrected version, October 2006: PDF)
Eyal Even-Dar, Michael Kearns, and Jennifer Wortman
In the 17th International Conference on Algorithmic Learning Theory (ALT 2006)
Eyal Even-Dar, Michael Kearns, and Jennifer Wortman
In the 17th International Conference on Algorithmic Learning Theory (ALT 2006)
Domain Adaptation and Multi-Source Learning
A Theory of Learning from Different Domains (Publisher's PDF)
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan
Machine Learning Journal (Special Issue on Learning from Multiple Sources), Volume 79, Numbers 1-2, Pages 151-175, 2010
(Supersedes the NeurIPS 07 paper)
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan
Machine Learning Journal (Special Issue on Learning from Multiple Sources), Volume 79, Numbers 1-2, Pages 151-175, 2010
(Supersedes the NeurIPS 07 paper)
Learning from Multiple Sources (Publisher's PDF)
Koby Crammer, Michael Kearns, and Jennifer Wortman
Journal of Machine Learning Research, Volume 9, Pages 1757-1774, 2008
(Supersedes the NeurIPS 06 paper)
Koby Crammer, Michael Kearns, and Jennifer Wortman
Journal of Machine Learning Research, Volume 9, Pages 1757-1774, 2008
(Supersedes the NeurIPS 06 paper)
Learning Bounds for Domain Adaptation
(PDF)
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman
In Advances in Neural Information Processing Systems 20 (NeurIPS 2007)
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman
In Advances in Neural Information Processing Systems 20 (NeurIPS 2007)
Learning from Multiple Sources
(PDF)
Koby Crammer, Michael Kearns, and Jennifer Wortman
In Advances in Neural Information Processing Systems 19 (NeurIPS 2006)
Koby Crammer, Michael Kearns, and Jennifer Wortman
In Advances in Neural Information Processing Systems 19 (NeurIPS 2006)
Learning from Data of Variable Quality
(PDF)
Koby Crammer, Michael Kearns, and Jennifer Wortman
In Advances in Neural Information Processing Systems 18 (NeurIPS 2005)
Koby Crammer, Michael Kearns, and Jennifer Wortman
In Advances in Neural Information Processing Systems 18 (NeurIPS 2005)
Social Networks and Collective Behavior
Behavioral Experiments on Biased Voting in Networks
(Publisher's PDF)
Michael Kearns, Stephen Judd, Jinsong Tan, and Jennifer Wortman
Proceedings of the National Academy of Sciences, Volume 106, Number 5, Pages 1347-1352, 2009
Michael Kearns, Stephen Judd, Jinsong Tan, and Jennifer Wortman
Proceedings of the National Academy of Sciences, Volume 106, Number 5, Pages 1347-1352, 2009
Learning from Collective Behavior
(PDF)
Michael Kearns and Jennifer Wortman
In the 21st Annual Conference on Learning Theory (COLT 2008)
Michael Kearns and Jennifer Wortman
In the 21st Annual Conference on Learning Theory (COLT 2008)
Viral Marketing and the Diffusion of Trends on Social Networks
(PDF)
Jennifer Wortman
University of Pennsylvania Technical Report MS-CIS-08-19, May 2008
In fulfillment of the Department of Computer and Information Science Written Preliminary Exam II
Jennifer Wortman
University of Pennsylvania Technical Report MS-CIS-08-19, May 2008
In fulfillment of the Department of Computer and Information Science Written Preliminary Exam II
Privacy-Preserving Belief Propagation and Sampling
(PDF)
Michael Kearns, Jinsong Tan, and Jennifer Wortman
In Advances in Neural Information Processing Systems 20 (NeurIPS 2007)
Winner of the Best Student Paper Award at the New York Academy of Sciences 2007 Symposium on ML
Michael Kearns, Jinsong Tan, and Jennifer Wortman
In Advances in Neural Information Processing Systems 20 (NeurIPS 2007)
Winner of the Best Student Paper Award at the New York Academy of Sciences 2007 Symposium on ML
Other Computer Science and Game Theory
Maintaining Equilibria During Exploration in Sponsored Search Auctions (Publisher's Page)
John Langford, Lihong Li, Yevgeniy Vorobeychik, and Jennifer Wortman
Algorithmica, Volume 58, Number 4, Pages 990-1021, 2010
(Supersedes the WINE 07 paper)
John Langford, Lihong Li, Yevgeniy Vorobeychik, and Jennifer Wortman
Algorithmica, Volume 58, Number 4, Pages 990-1021, 2010
(Supersedes the WINE 07 paper)
Maintaining Equilibria During Exploration in Sponsored Search Auctions
(PDF)
Jennifer Wortman, Yevgeniy Vorobeychik, Lihong Li, and John Langford
In the 3rd International Workshop on Internet and Network Economics (WINE 2007)
Jennifer Wortman, Yevgeniy Vorobeychik, Lihong Li, and John Langford
In the 3rd International Workshop on Internet and Network Economics (WINE 2007)
Sponsored Search with Contexts
(PDF)
Eyal Even-Dar, Michael Kearns, and Jennifer Wortman
In the 3rd International Workshop on Internet and Network Economics (WINE 2007)
(This longer version appeared in the WWW 2007 Third Workshop on Sponsored Search Auctions)
Eyal Even-Dar, Michael Kearns, and Jennifer Wortman
In the 3rd International Workshop on Internet and Network Economics (WINE 2007)
(This longer version appeared in the WWW 2007 Third Workshop on Sponsored Search Auctions)
Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms
(PDF; GAMUT website)
Eugene Nudelman, Jennifer Wortman, Yoav Shoham, and Kevin Leyton-Brown
In the 3rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2004)
(Short versions appeared at the Second World Congress of the Game Theory Society and the 15th Annual Conference on Game Theory)
Eugene Nudelman, Jennifer Wortman, Yoav Shoham, and Kevin Leyton-Brown
In the 3rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2004)
(Short versions appeared at the Second World Congress of the Game Theory Society and the 15th Annual Conference on Game Theory)
Miscellanea
Responsible Computing During COVID-19 and Beyond (full text at CACM)
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miroslav Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, and Marion Zepf
Communications of the ACM, Volume 64, Number 7, pages 30-32, July 2021
Solon Barocas, Asia J. Biega, Margarita Boyarskaya, Kate Crawford, Hal Daumé III, Miroslav Dudík, Benjamin Fish, Mary L. Gray, Brent Hecht, Alexandra Olteanu, Forough Poursabzi-Sangdeh, Luke Stark, Jennifer Wortman Vaughan, Hanna Wallach, and Marion Zepf
Communications of the ACM, Volume 64, Number 7, pages 30-32, July 2021
Using Search Queries to Understand Health Information Needs in Africa (long version on arxiv)
Rediet Abebe, Shawndra Hill, Jennifer Wortman Vaughan, Peter M. Small, and H. Andrew Schwartz
In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2019)
(A preliminary version appeared in the NeurIPS 2018 Workshop on ML for the Developing World)
Rediet Abebe, Shawndra Hill, Jennifer Wortman Vaughan, Peter M. Small, and H. Andrew Schwartz
In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2019)
(A preliminary version appeared in the NeurIPS 2018 Workshop on ML for the Developing World)
Ph.D. Thesis
Learning from Collective Preferences, Behavior, and Beliefs
(PDF)
Jennifer Wortman Vaughan
Doctoral Dissertation, University of Pennsylvania, 2009
Jennifer Wortman Vaughan
Doctoral Dissertation, University of Pennsylvania, 2009