3 citations · 4 across the 5 of their papers we have counts for
5 papers · 1 filter
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…
Fairness Testing through Extreme Value Theory
Verya Monjezi, Ashutosh Trivedi, Vladik Kreinovich +1
Data-driven software is increasingly being used as a critical component of automated decision-support systems. Since this class of software learns its logic from historical data, i…
Predicting Fairness of ML Software Configurations
Salvador Robles Herrera, Verya Monjezi, Vladik Kreinovich +2
This paper investigates the relationships between hyperparameters of machine learning and fairness. Data-driven solutions are increasingly used in critical socio-technical applicat…
Information-Theoretic Testing and Debugging of Fairness Defects in Deep Neural Networks
Verya Monjezi, Ashutosh Trivedi, Gang Tan +1
The deep feedforward neural networks (DNNs) are increasingly deployed in socioeconomic critical decision support software systems. DNNs are exceptionally good at finding minimal, s…