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 projects related to fairness and interpretability of AI systems as part of Microsoft's FATE group. 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 into my research in order to better understand and model human behavior in technological 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.
Within Microsoft, I have been leading efforts around transparency, intelligibility, and explanation under the umbrella of Aether, our company-wide initiative focused on responsible AI.
I am very active in the research community and probably say yes to more service work than I should. Among other things, I was recently the (Program and General) Co-Chair of HCOMP 2019, the Workshops Chair of NeurIPS 2019, the Tutorial Co-Chair of both NIPS 2017 and NeurIPS 2018, the Workshops Co-Chair of both EC 2017 and EC 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. We have no current openings, but check back later this year.
Looking for material on fairness? Here are my keynote at the FTC, my MSR webinar on Machine Learning and Fairness (with Hanna Wallach), and my FAT* tutorial on the Challenges of Incorporating Algorithmic Fairness into Industry Practice (video here) (with a bunch of awesome people).
Looking for material on transparency and intelligibility? Here are my book chapter (with Hanna Wallach) and my MSR webinar on Transparency and Intelligibility Throughout the Machine Learning Lifecycle.
My standard bio (for talk announcements, etc.) is here.
My new Microsoft Research webinar on Transparency and Intelligibility Throughout the Machine Learning Life Cycle is now available on demand.
I will be giving a keynote at the CHI Workshop Where is the Human? Bridging the Gap Between AI and HCI. See you in Glasgow!
Video is now available for the tutorial my phenomenal collaborators and I gave at FAT* 2019 on the Challenges of Incorporating Algorithmic Fairness into Industry Practice.
I will be giving a plenary talk at ALT 2019 titled "Why is fair machine learning hard and how can theory help?"
Edith Law and I will co-chair HCOMP 2019, the 7th AAAI Conference on Human Computation and Crowdsourcing! It will be held October 28-30, 2019 at Skamania Lodge outside of Portland, OR. Submissions due June 3.
I gave a keynote with a general-audience overview of the hairy issues around fairness, transparency, and intelligibility in machine learning at the recent FTC hearing on AI. Watch the video here.
A fully revamped and expanded version of my crowdsourcing survey for machine learning researchers is out in JMLR.
I recently had the chance to speak about ethical issues in AI at the 2018 Economist Innovation Summit.
I posted a transcript of my opening remarks at WiML 2017: Nine things I wish I had known the first time I came to NIPS.
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.