Jenn Wortman Vaughan

About

Hi! I’m Jenn Wortman Vaughan (opens in new tab), a researcher interested in the interaction between people and AI systems, with a focus on making these systems more responsible, transparent, and human-centered. My passion is for AI that augments, rather than replaces, human abilities. While my roots are in machine learning and algorithmic economics, I now often use human-subject experiments and qualitative methods to explore how people engage with AI systems in practice.

I am currently a Senior Principal Research Manager at Microsoft Research, New York City, a collaborative and interdisciplinary basic research lab, where I am part of the FATE group and a close collaborator of STAC. Before joining MSR, I was an Assistant Professor in the Computer Science Department at UCLA and a Computing Innovation Fellow at Harvard. I received my Ph.D. in Computer and Information Science from the University of Pennsylvania in 2009.

My recent research cuts across several themes:

• AI Transparency and Control. A central theme of my work is advancing responsible AI through transparency. I develop and evaluate methods—including model and dataset documentation, interpretability techniques, and approaches for communicating uncertainty—to help diverse stakeholders understand a system’s capabilities and limitations, and how to use or control its outputs. I take a human-centered perspective, focusing on whether transparency tools genuinely meet the needs of developers, policymakers, and end users.
• Appropriate Reliance, Human Agency, and Critical Thought. A growing direction in my research examines how AI systems can be designed to support appropriate reliance, preserve human agency, and encourage critical engagement. Building on themes of transparency, this work goes further by investigating how design can prompt reflection, encouraging users to question, interpret, and contextualize AI outputs.
• Human-Centered Evaluation of Generative AI. Another emerging theme of my research focuses on how to evaluate generative AI systems in ways that center human perspectives. I investigate where and how to incorporate human input, and how to balance human participation with automated approaches. This work includes participatory methods that integrate feedback from stakeholders directly affected by generative AI.
• Responsible AI in Industry Practice. I work to bridge the gap between responsible AI principles and industry realities. As a researcher embedded in industry, I ground recommendations for responsible AI processes in the challenges of real-world practice. My work has included qualitative studies of practitioners’ needs and challenges around documentation, fairness, and interpretability, revealing where existing tools fall short and how they can be adapted to practitioners’ workflows.
• Responsible Research Practices. Finally, my research examines how to strengthen responsible research practices in industry and academia. I develop methods to help researchers critically assess the limitations and potential societal consequences of their work, and contribute to community efforts to embed responsibility into research processes and publication standards.

See my website (opens in new tab) for more up-to-date information and a full publication list (opens in new tab). You can also occasionally find me on Twitter (opens in new tab) and Bluesky (opens in new tab).