Jennifer Wortman Vaughan

Jennifer Wortman Vaughan's AcademicInfluence.com Rankings

Computer Science

#3376

World Rank

#3542

Historical Rank

#1296

USA Rank

Algorithms

#423

World Rank

#428

Historical Rank

#40

USA Rank

Machine Learning

#2261

World Rank

#2289

Historical Rank

#133

USA Rank

Database

#8552

World Rank

#8959

Historical Rank

#1137

USA Rank

computer-science Degrees

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Computer Science

Jennifer Wortman Vaughan's Degrees

PhD Computer Science Stanford University
Masters Computer Science Stanford University
Bachelors Computer Science Carnegie Mellon University

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Why Is Jennifer Wortman Vaughan Influential?

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According to Wikipedia, Jennifer Wortman Vaughan is an American computer scientist and Senior Principal Researcher at Microsoft Research focusing mainly on building responsible artificial intelligence systems as part of Microsoft's Fairness, Accountability, Transparency, and Ethics in AI initiative. Jennifer is also a co-chair of Microsoft's Aether group on transparency that works on operationalizing responsible AI across Microsoft through making recommendations on responsible AI issues, technologies, processes, and best practices. Jennifer is also active in the research community, she served as the workshops chair and the program co-chair of the Conference on Neural Information Processing Systems in 2019 and 2021, respectively. She currently serves as Steering Committee member of the Association for Computing Machinery Conference on Fairness, Accountability and Transparency. Jennifer is also a senior advisor to Women in Machine Learning , an initiative co-founded by Jennifer in 2006 aiming to enhance the experience of women in Machine Learning.

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Jennifer Wortman Vaughan's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

A theory of learning from different domains (2010) (2505)
Datasheets for datasets (2018) (994)
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (2018) (471)
Learning Bounds for Domain Adaptation (2007) (440)
Manipulating and Measuring Model Interpretability (2018) (435)
Online Task Assignment in Crowdsourcing Markets (2012) (327)
Learning from Multiple Sources (2006) (313)
Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning (2020) (276)
Adaptive Task Assignment for Crowdsourced Classification (2013) (264)
Understanding the Effect of Accuracy on Trust in Machine Learning Models (2019) (241)
Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI (2020) (224)
Run the GAMUT: a comprehensive approach to evaluating game-theoretic algorithms (2004) (206)
The true sample complexity of active learning (2010) (176)
Behavioral experiments on biased voting in networks (2009) (150)
Exploration scavenging (2008) (133)
Incentivizing high quality crowdwork (2015) (122)
The Disparate Effects of Strategic Manipulation (2018) (122)
Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research (2017) (116)
A new understanding of prediction markets via no-regret learning (2010) (101)
Adaptive contract design for crowdsourcing markets: bandit algorithms for repeated principal-agent problems (2014) (90)
Efficient Market Making via Convex Optimization, and a Connection to Online Learning (2013) (86)
Online decision making in crowdsourcing markets: theoretical challenges (2013) (84)
The Communication Network Within the Crowd (2016) (82)
Toward fairness in AI for people with disabilities SBG@a research roadmap (2019) (70)
Censored exploration and the dark pool problem (2009) (70)
Complexity of combinatorial market makers (2008) (70)
An optimization-based framework for automated market-making (2010) (64)
A Human-Centered Agenda for Intelligible Machine Learning (2021) (54)
The Externalities of Exploration and How Data Diversity Helps Exploitation (2018) (49)
Group Fairness for the Allocation of Indivisible Goods (2019) (44)
Self-financed wagering mechanisms for forecasting (2008) (41)
Assessing the Fairness of AI Systems: AI Practitioners' Processes, Challenges, and Needs for Support (2021) (41)
Towards Social Norm Design for Crowdsourcing Markets (2012) (41)
Oracle-Efficient Online Learning and Auction Design (2016) (40)
Sponsored Search with Contexts (2007) (38)
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs (2021) (37)
Computational social science and social computing (2014) (36)
Regret to the best vs. regret to the average (2007) (35)
Risk-Sensitive Online Learning (2006) (35)
Learning from Data of Variable Quality (2005) (35)
Truthful Aggregation of Budget Proposals (2019) (33)
Using Search Queries to Understand Health Information Needs in Africa (2018) (28)
Regret Minimization With Concept Drift (2010) (27)
Evolution with Drifting Targets (2010) (26)
An axiomatic characterization of wagering mechanisms (2015) (25)
Privacy-Preserving Belief Propagation and Sampling (2007) (23)
Maintaining Equilibria During Exploration in Sponsored Search Auctions (2010) (22)
Belief Aggregation with Automated Market Makers (2015) (20)
Incentive-Compatible Forecasting Competitions (2018) (19)
The Possibilities and Limitations of Private Prediction Markets (2016) (17)
Mathematical foundations for social computing (2016) (16)
Removing arbitrage from wagering mechanisms (2014) (16)
An axiomatic characterization of adaptive-liquidity market makers (2013) (15)
Maintaining Equilibria During Exploration in Sponsored Search Auctions (2007) (15)
Weight of Evidence as a Basis for Human-Oriented Explanations (2019) (15)
Learning from Collective Behavior (2008) (14)
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence (2021) (14)
Designing Informative Securities (2012) (12)
GAM Changer: Editing Generalized Additive Models with Interactive Visualization (2021) (12)
Incentivizing High Quality Crowdwork (2015) (10)
A general volume-parameterized market making framework (2014) (10)
Cost function market makers for measurable spaces (2013) (10)
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits (2020) (10)
The Double Clinching Auction for Wagering (2017) (9)
Oracle-Efficient Learning and Auction Design (2016) (9)
Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets (2015) (6)
A Decomposition of Forecast Error in Prediction Markets (2017) (5)
Group Fairness for Indivisible Goods Allocation (2019) (4)
Market Making with Decreasing Utility for Information (2014) (4)
Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations (2023) (3)
No-Regret and Incentive-Compatible Online Learning (2020) (3)
Bounded Rationality in Wagering Mechanisms (2016) (3)
A Human in the Loop is Not Enough: The Need for Human-Subject Experiments in Facial Recognition (2020) (2)
The Disparate Effects of Strategic Classification (2018) (2)
The Unfair Externalities of Exploration (2017) (2)
A Human-Centered Interpretability Framework Based on Weight of Evidence (2021) (1)
Connections between markets and learning (2010) (1)
How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions? (2022) (1)
The Inescapability of Uncertainty (2017) (1)
Summarize with Caution: Comparing Global Feature Attributions (2021) (1)
Tutorial: Making Better Use of the Crowd (2017) (1)
The Possibilities and Limitations of Private Prediction Markets (2020) (0)
Belief Aggregation with Automated Market Makers (2015) (0)
An Equivalence between Wagering and Fair-Division Mechanisms (2019) (0)
Generation Probabilities Are Not Enough: Exploring the Effectiveness of Uncertainty Highlighting in AI-Powered Code Completions (2023) (0)
Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience (2023) (0)
Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets (2018) (0)
Special Issue on Responsible AI and Human-AI Interaction (2022) (0)
Learning from collective preferences, behavior, and beliefs (2009) (0)
Incentives and the crowd (2017) (0)
Interpretable Distribution Shift Detection using Optimal Transport (2022) (0)
Working Draft : This is work in progress ! Datasheets for Datasets ∗ (2018) (0)
Crowdsourcing gives us a way to leverage the complementary strengths of humans and machines . But how do we solve the problem of low-quality crowdwork ? (2017) (0)

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Jennifer Wortman Vaughan's Academic­Influence.com Rankings

Jennifer Wortman Vaughan's Degrees

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Why Is Jennifer Wortman Vaughan Influential?

Jennifer Wortman Vaughan's Published Works

Published Works

Other Resources About Jennifer Wortman Vaughan

What Schools Are Affiliated With Jennifer Wortman Vaughan?

Jennifer Wortman Vaughan's AcademicInfluence.com Rankings