research
∙
02/02/2023
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
Fair machine learning methods seek to train models that balance model pe...
share
research
∙
09/20/2022
Counterfactual Mean-variance Optimization
We study a new class of estimands in causal inference, which are the sol...
research
∙
06/29/2022
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale
The success of DNNs is driven by the counter-intuitive ability of over-p...
research
∙
05/26/2022
Flexible Group Fairness Metrics for Survival Analysis
Algorithmic fairness is an increasingly important field concerned with d...
research
∙
02/10/2022
Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings
Many popular algorithmic fairness measures depend on the joint distribut...
research
∙
09/01/2021
FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes
Methods for building fair predictors often involve tradeoffs between fai...
research
∙
05/21/2021
Algorithmic Audit of Italian Car Insurance: Evidence of Unfairness in Access and Pricing
We conduct an audit of pricing algorithms employed by companies in the I...
research
∙
04/06/2021
Clustering Students and Inferring Skill Set Profiles with Skill Hierarchies
Cognitive diagnosis models (CDMs) are a popular tool for assessing stude...
research
∙
03/29/2021
Comment on "Statistical Modeling: The Two Cultures" by Leo Breiman
Motivated by Breiman's rousing 2001 paper on the "two cultures" in stati...
research
∙
09/07/2020
Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds
Algorithmic fairness is a topic of increasing concern both within resear...
research
∙
02/20/2017
