Jenn Wortman Vaughan

Welcome!

I am a Senior Principal Researcher at Microsoft Research, New York City, a collaborative and interdisciplinary basic research lab.

My research background is in machine learning and algorithmic economics. These days I spend the bulk of my time on responsible AI—specifically transparency, interpretability, and fairness of AI systems—as part of Microsoft's FATE group and Co-Chair of Microsoft's Aether working group on transparency. I am interested in the interaction between people and AI, and have often studied this interaction in the context of prediction markets and other crowdsourcing systems. My passion is for AI that augments, rather than replaces, human abilities. A big fraction of my work has been theoretical—I like a nice clean model that captures the essence of a problem and provable guarantees—but, thanks to the amazing interdisciplinary environment where I work, I've begun incorporating more experiments and even qualitative methods into my research in order to better understand human behavior in sociotechnical systems. I occasionally speak about societal issues around AI.

For a more complete picture of my research, take a look at some of my publications.

I am very active in the research community. I will be serving as Program Co-Chair of NeurIPS 2021. I have also recently served as the (Program and General) Co-Chair of HCOMP 2019, the Workshops Chair of NeurIPS 2019, the Tutorial Co-Chair of both NeurIPS 2017 and 2018, the Workshops Co-Chair of both EC 2017 and 2018, the Press Co-Chair of ICML 2019, and the Secretary-Treasurer of SigEcom from 2015-2019. I am currently a Steering Committee Member of ACM FAccT and a Senior Advisor to WiML (which I co-founded back in 2006).

At MSR I have had the opportunity to work with an amazing set of interns. Typically our interns are Ph.D. students with strong publication records in areas relevant to our lab. The 2021 application for FATE internships is here.

I reluctantly tweet as @jennwvaughan.

Quick Links and Resources

Transparency and intelligibility: book chapter, webinar, podcast episode, trustworthly ml seminar, NeurIPS workshop talk

Fairness: FTC keynote, webinar, tutorial overview and video from FAT*

Crowdsourcing: tutorial overview and video from NeurIPS, JMLR survey article

My standard bio (for talk announcements, etc.) is here.

What's New?

I will be serving as Program Co-Chair of NeurIPS 2021.

I'll be speaking about fairness and transparency at the NYU AI School for undergrads interested in machine learning and AI.

The FATE group is hiring interns for 2021! See our ad here. If you are a PhD candidate with a strong background in visualization for machine learning, here is another opportunity for you.

I've been giving a bunch of virtual talks on human-centered approaches to intelligible machine learning. You can view videos from the Trustworthy ML Initiative seminar (longer version) or the NeurIPS HAMLETS workshop (shorter version).

Listen to me talk about AI transparency, my research journey from machine learning theory to human-centered responsible AI, and pessimism as a super power on the Radical AI podcast.

More activities...

Background

I came to MSR-NYC from UCLA where I was an Assistant Professor in the Computer Science Department. Prior to that I spent a year as a Computing Innovation Fellow at Harvard University where I was a member of the EconCS group and the Theory group, and an affiliate of the Center for Research on Computation and Society.

I received my Ph.D. in Computer and Information Science from the University of Pennsylvania in 2009. I was extremely lucky to be advised by Michael Kearns. My doctoral dissertation, Learning from Collective Preferences, Behavior, and Beliefs, introduced a series of new learning models and algorithms designed to address the problems commonly faced when aggregating local information across large population, and was awarded Penn's Rubinoff award for innovative applications of computer technology. During my time at Penn, I spent two fun summers interning in New York, first with the Machine Learning and Microeconomics groups at Yahoo! Research and then in the research group at Google.

Before coming to Penn, I completed a Masters in Computer Science at Stanford where I got my first taste of research working with the Multiagent Group. Further back in the day, I was a carefree undergrad at BU.

You might remember me as Jenn Wortman. When I got married, I took Vaughan (pronounced "von") as my "official" last name and moved Wortman to my middle name. This never fails to confuse people. I use both names together professionally and prefer that you do too.

Get In Touch

The best way to reach me is by email. I am jenn at microsoft.com.