51 citations · 69 across the 12 of their papers we have counts for
13 papers
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
Akshaj Kumar Veldanda, Ivan Brugere, Sanghamitra Dutta +2
Fair machine learning methods seek to train models that balance model performance across demographic subgroups defined over sensitive attributes like race and gender. Although sens…
Counterfactual Mean-variance Optimization
Kwangho Kim, Alan Mishler, José R. Zubizarreta
We study a counterfactual mean-variance optimization, where the mean and variance are defined as functionals of counterfactual distributions. The optimization problem defines the o…
Flexible Group Fairness Metrics for Survival Analysis
Raphael Sonabend, Florian Pfisterer, Alan Mishler +4
Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorit…
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale
Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen +3
The success of DNNs is driven by the counter-intuitive ability of over-parameterized networks to generalize, even when they perfectly fit the training data. In practice, test error…
Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings
Alan Mishler, Niccolò Dalmasso
Many popular algorithmic fairness measures depend on the joint distribution of predictions, outcomes, and a sensitive feature like race or gender. These measures are sensitive to d…
FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes
Alan Mishler, Edward Kennedy
Methods for building fair predictors often involve tradeoffs between fairness and accuracy and between different fairness criteria, but the nature of these tradeoffs varies. Recent…
Algorithmic Audit of Italian Car Insurance: Evidence of Unfairness in Access and Pricing
Alessandro Fabris, Alan Mishler, Stefano Gottardi +4
We conduct an audit of pricing algorithms employed by companies in the Italian car insurance industry, primarily by gathering quotes through a popular comparison website. While ack…
Clustering Students and Inferring Skill Set Profiles with Skill Hierarchies
Alan Mishler, Rebecca Nugent
Cognitive diagnosis models (CDMs) are a popular tool for assessing students' mastery of sets of skills. Given a set of skills tested on an assessment, students are classified i…
When the Oracle Misleads: Modeling the Consequences of Using Observable Rather than Potential Outcomes in Risk Assessment Instruments
Alan Mishler, Niccolò Dalmasso
Risk Assessment Instruments (RAIs) are widely used to forecast adverse outcomes in domains such as healthcare and criminal justice. RAIs are commonly trained on observational data…
Comment on "Statistical Modeling: The Two Cultures" by Leo Breiman
Matteo Bonvini, Alan Mishler, Edward H. Kennedy
Motivated by Breiman's rousing 2001 paper on the "two cultures" in statistics, we consider the role that different modeling approaches play in causal inference. We discuss the rela…
Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds
Alan Mishler, Edward H. Kennedy, Alexandra Chouldechova
In domains such as criminal justice, medicine, and social welfare, decision makers increasingly have access to algorithmic Risk Assessment Instruments (RAIs). RAIs estimate the ris…
Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms
Ashley I Naimi, Alan E Mishler, Edward H Kennedy
Unlike parametric regression, machine learning (ML) methods do not generally require precise knowledge of the true data generating mechanisms. As such, numerous authors have advoca…
Filtering Tweets for Social Unrest
Alan Mishler, Kevin Wonus, Wendy Chambers +1
Since the events of the Arab Spring, there has been increased interest in using social media to anticipate social unrest. While efforts have been made toward automated unrest predi…