45 citations · 48 across the 8 of their papers we have counts for
8 papers
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…
Heimdall: Formally Verified Automated Migration of Legacy eBPF Programs to Rust
Vishnu Asutosh Dasu, Monika Santra, Md Rafi Ur Rashid +3
Extended Berkeley Packet Filter (eBPF) programs are kernel extensions used for networking, observability, and security enforcement in the Linux kernel. The in-kernel eBPF verifier…
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…
Probabilistic Guarantees for Practical LIA Loop Invariant Automation
Ashish Kumar, Jilaun Zhang, Saeid Tizpaz-Niari +1
Despite the crucial need for formal safety and security verification of programs, discovering loop invariants remains a significant challenge. Static analysis is a primary techniqu…
NeuFair: Neural Network Fairness Repair with Dropout
Vishnu Asutosh Dasu, Ashish Kumar, Saeid Tizpaz-Niari +1
This paper investigates neuron dropout as a post-processing bias mitigation for deep neural networks (DNNs). Neural-driven software solutions are increasingly applied in socially c…
Fairness-aware Configuration of Machine Learning Libraries
Saeid Tizpaz-Niari, Ashish Kumar, Gang Tan +1
This paper investigates the parameter space of machine learning (ML) algorithms in aggravating or mitigating fairness bugs. Data-driven software is increasingly applied in social-c…
A Derivative-based Parser Generator for Visibly Pushdown Grammars
Xiaodong Jia, Ashish Kumar, Gang Tan
In this paper, we present a derivative-based, functional recognizer and parser generator for visibly pushdown grammars. The generated parser accepts ambiguous grammars and produces…