7 citations · 8 across the 8 of their papers we have counts for
8 papers
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…
An LLM Agentic Approach for Legal-Critical Software: A Case Study for Tax Prep Software
Sina Gogani-Khiabani, Ashutosh Trivedi, Diptikalyan Saha +1
Large language models (LLMs) show promise for translating natural-language statutes into executable logic, but reliability in legally critical settings remains challenging due to a…
Technical Challenges in Maintaining Tax Prep Software with Large Language Models
Sina Gogani-Khiabani, Varsha Dewangan, Nina Olson +2
As the US tax law evolves to adapt to ever-changing politico-economic realities, tax preparation software plays a significant role in helping taxpayers navigate these complexities.…
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…
Metamorphic Debugging for Accountable Software
Saeid Tizpaz-Niari, Shiva Darian, Ashutosh Trivedi
As the laws have become more complicated and enormous, the role of software systems in navigating and understanding these intricacies has become more critical. Given their socio-ec…
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…
On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software
Dananjay Srinivas, Rohan Das, Saeid Tizpaz-Niari +2
Due to the ever-increasing complexity of income tax laws in the United States, the number of US taxpayers filing their taxes using tax preparation software (henceforth, tax softwar…