1 citations · 2 across the 8 of their papers we have counts for
4 papers · 1 filter
Fairness Invariants: A Relational Approach to Explaining and Mitigating Fairness Bugs
Ranit Debnath Akash, Ashish Kumar, Gang Tan +1
Data-driven software systems are increasingly deployed in high-stakes socio-economic domains, from criminal justice to financial lending. However, these systems often exhibit indiv…
On the Robustness of Fairness Practices: A Causal Framework for Systematic Evaluation
Verya Monjezi, Ashish Kumar, Ashutosh Trivedi +2
Machine learning (ML) algorithms are increasingly deployed to make critical decisions in socioeconomic applications such as finance, criminal justice, and autonomous driving. Howev…
Uncovering Discrimination Clusters: Quantifying and Explaining Systematic Fairness Violations
Ranit Debnath Akash, Ashish Kumar, Verya Monjezi +4
Fairness in algorithmic decision-making is often framed in terms of individual fairness, which requires that similar individuals receive similar outcomes. A system violates individ…
FairLay-ML: Intuitive Debugging of Fairness in Data-Driven Social-Critical Software
Normen Yu, Luciana Carreon, Gang Tan +1
Data-driven software solutions have significantly been used in critical domains with significant socio-economic, legal, and ethical implications. The rapid adoptions of data-driven…