Algorithms and Explanations
245 Sullivan St
New York, NY 10012
USA
Abstract:
Explanation has long been deemed a crucial aspect of accountability. By requiring that powerful actors explain the bases of their decisions — the logic goes — we reduce the risks of error, abuse, and arbitrariness, thus producing more socially desirable decisions. Decisionmaking processes employing machine learning algorithms and similar data-driven approaches complicate this equation. Such approaches promise to refine and improve the accuracy and efficiency of decisionmaking processes, but the logic and rationale behind each decision remains opaque to human understanding. The conference will grapple with the question of when and to what extent decisionmakers should be legally or ethically obligated to provide humanly meaningful explanations of individual decisions to those who are affected or to society at large.
List of Speakers:
Julius Adebayo, FastForward Labs
Guruduth Banavar, IBM Watson Lab
Solon Barocas, Microsoft Research
Enrico Bertini, NYU (Engineering)
Kiel Brennan-Marquez, NYU (Law)
Julie Brill, Hogan Lovells
Jim Burch, Police Foundation
Jenna Burrell, UC Berkeley (Information)
Federico Cabitza, Università degli Studi di Milano-Bicocca (Italy)
Rich Caruana, Cornell (CS)
Alexandra Chouldechova, Carnegie Mellon (CS)
Anupam Datta, Carnegie Mellon (CS)
Deven Desai, Georgia Tech (Law)
Nick Diakopoulos, University of Maryland (Journalism)
Brad Greenberg, Yale ISP (Law)
Krishna Gummadi, MPI-SWS (Germany)
Jeremy Heffner, Hunchlab
Alison Howard, Microsoft
Zachary Lipton, UCSD (Biomedical Informatics)
Gilad Lotan, Buzzfeed
Frank Pasquale, University of Maryland (Law)
Foster Provost, NYU (Stern)
Dan Raviv, Lendbuzz
Aaron Rieke, Upturn
Paul Rifelj, Wisconsin Public Defenders
Andrea Roth, UC Berkeley (Law)
Andrew Selbst, Information Society Project
Kevin Stack, Vanderbilt (Law)
Katherine Strandburg, NYU (Law)
Jer Thorpe, Office for Creative Research
Sandra Wachter – Alan Turing Institute
Duncan Watts, Microsoft Research