Sendhil Mullainathan

Sendhil Mullainathan
Are you Sendhil Mullainathan?

Claim your profile, edit publications, add additional information:

Contact Details

Name
Sendhil Mullainathan
Affiliation
Location

Pubs By Year

Pub Categories

 
Computer Science - Artificial Intelligence (2)
 
Computer Science - Computer Science and Game Theory (1)
 
Statistics - Machine Learning (1)
 
Computer Science - Learning (1)
 
Computer Science - Data Structures and Algorithms (1)
 
Computer Science - Computers and Society (1)

Publications Authored By Sendhil Mullainathan

A broad range of on-line behaviors are mediated by interfaces in which people make choices among sets of options. A rich and growing line of work in the behavioral sciences indicate that human choices follow not only from the utility of alternatives, but also from the choice set in which alternatives are presented. In this work we study comparison-based choice functions, a simple but surprisingly rich class of functions capable of exhibiting so-called choice-set effects. Read More

Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups. We formalize three fairness conditions that lie at the heart of these debates, and we prove that except in highly constrained special cases, there is no method that can satisfy these three conditions simultaneously. Moreover, even satisfying all three conditions approximately requires that the data lie in an approximate version of one of the constrained special cases identified by our theorem. Read More

An increasing number of domains are providing us with detailed trace data on human decisions in settings where we can evaluate the quality of these decisions via an algorithm. Motivated by this development, an emerging line of work has begun to consider whether we can characterize and predict the kinds of decisions where people are likely to make errors. To investigate what a general framework for human error prediction might look like, we focus on a model system with a rich history in the behavioral sciences: the decisions made by chess players as they select moves in a game. Read More