I am a Senior 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, accountability, transparency, and ethics, or FATE. 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 what I do, take a look at some of my publications.
I am very active in the research community and probably say yes to more service work than I should. Among other things, I am currently the (Program and General) Co-Chair of HCOMP 2019, the Workshops Chair of NeurIPS 2019, the Press Co-Chair of ICML 2019, the Secretary-Treasurer of SigEcom, a Steering Committee Member of FAT*, and a Senior Advisor to WiML (which I co-founded back in 2006). I was also recently the Tutorial Co-Chair of both NIPS 2017 and NeurIPS 2018, and the Workshops Co-Chair of both EC 2017 and EC 2018.
At MSR I have had the opportunity to work with an amazing set of interns including Chien-Ju Ho (who was also my PhD student at UCLA!), Alice Gao, Hoda Heidari, Ming Yin, Rachel Cummings, Ryan Rogers, Nika Haghtalab, Rupert Freeman, Rediet Abebe, Manish Raghavan, Forough Poursabzi-Sangdeh, David Alvarez Melis, Ken Holstein, and Lily Hu. Typically our interns are Ph.D. students with strong publication records in areas relevant to our lab.
If you are looking for FATE material, you may be interested in my keynote at the FTC, my MSR webinar on machine learning and fairness (with Hanna Wallach), or my FAT* tutorial on the Challenges of Incorporating Algorithmic Fairness into Industry Practice (with a bunch of awesome people).
My standard bio (for talk announcements, etc.) is here.
My phenomenal collaborators and I will be giving tutorial at FAT* 2019 on the Challenges of Incorporating Algorithmic Fairness into Industry Practice. Webcast will be available on January 29.
Microsoft Research in NYC is looking for summer interns in FATE (fairness, accountability, transparency, and ethics of AI), economics and computation, machine learning, and computational social science too. Apply now! And if you'd like to work with me in particular, send me an email to make sure I see your application!
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.