Algorithms make predictions about people constantly. The spread of
such prediction systems has raised concerns that machine learning
algorithms may exhibit problematic behavior, especially against
individuals from marginalized groups. This talk will provide an
overview of research building a theory of “responsible” machine
learning. It will highlight a notion of fairness in prediction, called
Multicalibration (ICML’18), which requires predictions to be
well-calibrated, not simply overall, but on every group that can be
meaningfully identified from data. This “multi-group” approach
strengthens the guarantees of group fairness definitions, without
incurring the costs (statistical and computational) associated with
individual-level protections. Additionally, a new paradigm will be
presented for learning, Outcome Indistinguishability (STOC’21), which
provides a broad framework for learning predictors satisfying formal
guarantees of responsibility. Finally, the threat of Undetectable
Backdoors (FOCS’22) will be discussed which represent a serious
challenge for building trust in machine learning models.
Bio:
Michael P. Kim is a postdoctoral research fellow at the Miller
Institute for Basic Research in Science at UC Berkeley, hosted by Shafi
Goldwasser. Before this, Kim completed his Ph.D. in computer science at
Stanford University, advised by Omer Reingold. Kim’s research
addresses basic questions about the appropriate use of machine learning
algorithms that make predictions about people. More generally, Kim is
interested in how the computational lens (i.e., algorithms and
complexity theory) can provide insights into emerging societal and
scientific challenges.
