271 citations · 373 across the 12 of their papers we have counts for
13 papers
Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective
Avi Schwarzschild, Max Cembalest, Karthik Rao +2
As neural networks increasingly make critical decisions in high-stakes settings, monitoring and explaining their behavior in an understandable and trustworthy manner is a necessity…
Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation
I. Elizabeth Kumar, Keegan E. Hines, John P. Dickerson
Credit is an essential component of financial wellbeing in America, and unequal access to it is a large factor in the economic disparities between demographic groups that exist tod…
Repairing Regressors for Fair Binary Classification at Any Decision Threshold
Kweku Kwegyir-Aggrey, A. Feder Cooper, Jessica Dai +3
We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds. By decreasing the statistical dist…
Counterfactual Explanations for Machine Learning: Challenges Revisited
Sahil Verma, John Dickerson, Keegan Hines
Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models. They provide ``what if'' feedback of the form `…
Amortized Generation of Sequential Algorithmic Recourses for Black-box Models
Sahil Verma, Keegan Hines, John P. Dickerson
Explainable machine learning (ML) has gained traction in recent years due to the increasing adoption of ML-based systems in many sectors. Algorithmic Recourses (ARs) provide "what…
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Sahil Verma, Varich Boonsanong, Minh Hoang +3
Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understa…