2 citations · 2 across the 8 of their papers we have counts for
8 papers
Revisiting Fairness Impossibility with Endogenous Behavior
Elizabeth Maggie Penn, John W. Patty
In many real-world settings, institutions can and do adjust the consequences attached to algorithmic classification decisions, such as the size of fines, sentence lengths, or benef…
Classification in Equilibrium: Structure of Optimal Decision Rules
Elizabeth Maggie Penn, John W. Patty
This paper characterizes optimal classification when individuals adjust their behavior in response to the classification rule. We model the interaction between a designer and a pop…
Accuracy Is (Generically) Bad For Compliance
John W. Patty, Elizabeth Maggie Penn
We demonstrate that the set of cost distributions under which the optimal strategy for maximizing compliance (or more generally, effort) in a binary choice environment is identical…
Optimal classification with endogenous behavior
Elizabeth Maggie Penn
I consider the problem of classifying individual behavior in a simple setting of outcome performativity where the behavior the algorithm seeks to classify is itself dependent on th…
Algorithmic Fairness with Feedback
John W. Patty, Elizabeth Maggie Penn
The field of algorithmic fairness has rapidly emerged over the past 15 years as algorithms have become ubiquitous in everyday lives. Algorithmic fairness traditionally considers st…
Algorithms, Incentives, and Democracy
Elizabeth Maggie Penn, John W. Patty
Classification algorithms are increasingly used in areas such as housing, credit, and law enforcement in order to make decisions affecting peoples' lives. These algorithms can chan…
Ban The Box? Information, Incentives, and Statistical Discrimination
John W. Patty, Elizabeth Maggie Penn
"Banning the Box" refers to a policy campaign aimed at prohibiting employers from soliciting applicant information that could be used to statistically discriminate against categori…
Algorithmic Fairness and Statistical Discrimination
John W. Patty, Elizabeth Maggie Penn
Algorithmic fairness is a new interdisciplinary field of study focused on how to measure whether a process, or algorithm, may unintentionally produce unfair outcomes, as well as wh…