[100th TrustML Young Scientist Seminar] Strategic Classification: Learning With Data That ‘Behaves’

This talk will be held in a hybrid format, both in person at AIP Open Space of RIKEN AIP (Nihonbashi office) and online by Zoom. Meeting room 3 & 4, RIKEN Tokyo Office (Nihonbashi): *only available to AIP researchers.

DATE, TIME & LOCATION
Tuesday, October 14th, 10:00 - 11:00, RIKEN AIP Nihombashi Office, Open Space

TITLE
Strategic Classification: Learning With Data That ‘Behaves’

ABSTRACT
The growing success of machine learning across a wide array of domains
has made it for use also in the social domain. But humans are not your
conventional input: they have goals, beliefs, and aspirations, and
take action to promote their own self-interests. Given that standard
learning methods are not designed to handle inputs that "behave", it
is natural to ask: how should we design learning systems when we know
they will be deployed and used in social environments?
As a starting point, I will present the problem of strategic
classification, in which users can modify their features, at a cost,
to obtain favorable predictions. I will then describe some of our work
in this field, demonstrating how even mild forms of strategic behavior
can dramatically transform the learning problem, and the role game
theory can play in addressing some of the new challenges that arise.
Finally, I will argue for strategic classification as a framework that
can be useful for formally reasoning about learning under user
behavior in general, and which holds potential for weaving more
elaborate forms of economic modeling into the learning pipeline.

BIO
Nir Rosenfeld is an assistant professor of Computer Science at the
Technion, where he is head of the Behavioral Machine Learning lab,
working on problems at the intersection of machine learning and human
behavior. Before joining the Technion he was a postdoc at Harvard's
School of Engineering and Applied Sciences (SEAS), where he was a
member of the EconCS group, a fellow of the Center for Research on
Computation and Society (CRCS), and a fellow of the Harvard Data
Science Initiative (HDSI). He holds a BSc in Computer Science and
Psychology and an MSc and PhD in Computer Science, all from the Hebrew
University.