Quick links

Fairness Through Awareness

Date and Time
Wednesday, September 21, 2011 - 4:30pm to 5:30pm
Computer Science Small Auditorium (Room 105)
Distinguished Colloquium Series Speaker
Sanjeev Arora
"Why was I not shown this advertisement? Why was my loan application denied? Why was I denied admission to this university?"

In this work we initiate the formal study of fairness in classification, where the goal is to prevent discrimination against protected population subgroups in classification systems while simultaneously preserving utility for the party carrying out the classification (eg, the advertiser, bank, or admissions committee).

We argue that a classification is fair only when individuals who are similar with respect to the classification task at hand are treated similarly, and this in turn requires understanding of sub cultures of the population. Similarity metrics are applied in many contexts, but these are often hidden. Our work explicitly exposes the metric, opening it to public debate.

We then formalize and show how to achieve fairness in classification, given a similarity metric. We also give conditions on the metric under which our "local" notion ensures statistical parity: namely that the demographics of those receiving any given classification are the same as the demographics of the underlying population. In a complementary setting, we propose tools for what can be viewed as "fair affirmative action." Namely, we give methods for guaranteeing statistical parity for a group while treating similar individuals as similarly as possible.

Finally, we discuss the relationship of fairness to privacy: to what extent might fairness imply privacy, and is it possible to employ tools developed in the context of differential privacy in order to obtain fairness.

Joint work with Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel.

Cynthia Dwork '79, Distinguished Scientist at Microsoft Research, is the world's foremost expert on placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is differential privacy, a strong privacy guarantee permitting highly accurate data analysis. Dr. Dwork has also made seminal contributions in cryptography and distributed computing, and is a recipient of the Edsger W. Dijkstra Prize, recognizing some of her earliest work establishing the pillars on which every fault-tolerant system has been built for decades. She is a member of the US National Academy of Engineering and a Fellow of the American Academy of Arts and Sciences.

Follow us: Facebook Twitter Linkedin