activity
20182020
most citedAbstracting Fairness: Oracles, Metrics, and Interpretability

3 citations · 3 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CY2020

Individual Fairness in Pipelines

Cynthia Dwork, Christina Ilvento, Meena Jagadeesan

It is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline com…

cs.LG20203 cited

Abstracting Fairness: Oracles, Metrics, and Interpretability

Cynthia Dwork, Christina Ilvento, Guy N. Rothblum +1

It is well understood that classification algorithms, for example, for deciding on loan applications, cannot be evaluated for fairness without taking context into account. We exami…

cs.CR2019

Implementing the Exponential Mechanism with Base-2 Differential Privacy

Christina Ilvento

Despite excellent theoretical support, Differential Privacy (DP) can still be a challenge to implement in practice. In part, this challenge is due to the concerns associated with t…

cs.GT2019

Multi-Category Fairness in Sponsored Search Auctions

Shuchi Chawla, Christina Ilvento, Meena Jagadeesan

Fairness in advertising is a topic of particular concern motivated by theoretical and empirical observations in both the computer science and economics literature. We examine the p…

cs.LG2019

Metric Learning for Individual Fairness

Christina Ilvento

There has been much discussion recently about how fairness should be measured or enforced in classification. Individual Fairness [Dwork, Hardt, Pitassi, Reingold, Zemel, 2012], whi…

cs.LG2018

Fairness Under Composition

Cynthia Dwork, Christina Ilvento

Algorithmic fairness, and in particular the fairness of scoring and classification algorithms, has become a topic of increasing social concern and has recently witnessed an explosi…