4 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Data Acquisition for Improving Model Fairness using Reinforcement Learning
Jahid Hasan, Romila Pradhan
Machine learning systems are increasingly being used in critical decision making such as healthcare, finance, and criminal justice. Concerns around their fairness have resulted in…
cs.LG2024★ 1 cited
Example-based Explanations for Random Forests using Machine Unlearning
Tanmay Surve, Romila Pradhan
Tree-based machine learning models, such as decision trees and random forests, have been hugely successful in classification tasks primarily because of their predictive power in su…
cs.LG2021★ 4 cited
Interpretable Data-Based Explanations for Fairness Debugging
Romila Pradhan, Jiongli Zhu, Boris Glavic +1
A wide variety of fairness metrics and eXplainable Artificial Intelligence (XAI) approaches have been proposed in the literature to identify bias in machine learning models that ar…