[AI Seminar] AI Lunch -- Room Change -- Jamie Morgenstern -- October 4

[Ellen Vitercik]

Dear faculty and students,

This is a reminder that AI lunch will be held tomorrow, Tuesday, October
4th, at noon in *NSH 1507. Please note the room change. *To learn more
about the seminar and lunch, please visit the AI Lunch webpage
<http://www.cs.cmu.edu/~aiseminar/>.

Jamie Morgenstern <https://www.cis.upenn.edu/~jamiemor/>, a postdoc at
UPenn and a CMU alum, will give a talk titled "Towards a Theory of Fairness
in Machine Learning."

*Abstract: *Algorithm design has moved from being a tool used exclusively
for designing systems to one used to present people with personalized
content, advertisements, and other economic opportunities. Massive amounts
of information is recorded about people's online behavior including the
websites they visit, the advertisements they click on, their search
history, and their IP address. Algorithms then use this information for
many purposes: to choose which prices to quote individuals for airline
tickets, which advertisements to show them, and even which news stories to
promote. These systems create new challenges for algorithm design. When a
person's behavior influences the prices they may face in thefuture, they
may have a strong incentive to modify their behavior to improve their
long-term utility; therefore, these algorithms' performance should be
resilient to strategic manipulation. Furthermore, when an algorithm makes
choices that affect people's everyday lives, the effects of these choices
raise ethical concerns such as whether the algorithm's behavior violates
individuals' privacy or whether the algorithm treats people fairly.

Machine learning algorithms in particular have received much attention for
exhibiting bias, or unfairness, in a large number of contexts. In this
talk, I will describe my recent work on developing a definition of fairness
for machine learning. One definition of fairness, encoding the notion of
'fair equality of opportunity', informally, states that if one person has
higher expected quality than another person, the higher quality person
should be given at least as much opportunity as the lower quality person. I
will present a result characterizing the performance degradation of
algorithms which satisfy this condition in the contextual bandits setting.
To complement these theoretical results, I then present the results of
several empirical evaluations of fair algorithms.

I will also briefly describe my work on designing algorithms whose
performance guarantees are resilient to strategic manipulation of their
inputs, and machine learning for optimal auction design.

*Speaker bio:* Jamie Morgenstern is a Warren Center postdoctoral fellow in
Computer Science and Economics at the University of Pennsylvania. She
received her Ph.D. in Computer Science from Carnegie Mellon University in
2015, and her B.S. in Computer Science and B.A. in Mathematics from the
University of Chicago in 2010. Her research focuses on machine learning for
mechanism design, fairness in machine learning, and algorithmic game
theory. She received a Microsoft Women's Research Scholarship, an NSF
Graduate Research Fellowship, and a Simons Award for Graduate Students in
Theoretical Computer Science.
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