1 citations · 2 across the 8 of their papers we have counts for
9 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…
Chain-of-Thought Driven Adversarial Scenario Extrapolation for Robust Language Models
Md Rafi Ur Rashid, Vishnu Asutosh Dasu, Ye Wang +2
Large Language Models (LLMs) exhibit impressive capabilities, but remain susceptible to a growing spectrum of safety risks, including jailbreaks, toxic content, hallucinations, and…
Attention Pruning: Automated Fairness Repair of Language Models via Surrogate Simulated Annealing
Vishnu Asutosh Dasu, Md Rafi ur Rashid, Vipul Gupta +2
This paper explores pruning attention heads as a post-processing bias mitigation method for large language models (LLMs). Modern AI systems such as LLMs are expanding into sensitiv…
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…
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…