2 citations · 3 across the 10 of their papers we have counts for
10 papers
Comparative Analysis of FOLD-SE vs. FOLD-R++ in Binary Classification and XGBoost in Multi-Category Classification
Akshay Murthy, Shawn Sebastian, Manil Shangle +3
Recently, the demand for Machine Learning (ML) models that can balance accuracy, efficiency, and interpreability has grown significantly. Traditionally, there has been a tradeoff b…
P2C: Path to Counterfactuals
Sopam Dasgupta, Sadaf MD Halim, Joaquín Arias +2
Machine-learning models are increasingly driving decisions in high-stakes settings, such as finance, law, and hiring, thus, highlighting the need for transparency. However, the key…
MC3G: Model Agnostic Causally Constrained Counterfactual Generation
Sopam Dasgupta, Sadaf MD Halim, Joaquín Arias +2
Machine learning models increasingly influence decisions in high-stakes settings such as finance, law and hiring, driving the need for transparent, interpretable outcomes. However,…
Generating Causally Compliant Counterfactual Explanations using ASP
Sopam Dasgupta
This research is focused on generating achievable counterfactual explanations. Given a negative outcome computed by a machine learning model or a decision system, the novel CoGS ap…
CoGS: Model Agnostic Causality Constrained Counterfactual Explanations using goal-directed ASP
Sopam Dasgupta, Joaquín Arias, Elmer Salazar +1
Machine learning models are increasingly used in critical areas such as loan approvals and hiring, yet they often function as black boxes, obscuring their decision-making processes…
CoGS: Causality Constrained Counterfactual Explanations using goal-directed ASP
Sopam Dasgupta, Joaquín Arias, Elmer Salazar +1
Machine learning models are increasingly used in areas such as loan approvals and hiring, yet they often function as black boxes, obscuring their decision-making processes. Transpa…
CFGs: Causality Constrained Counterfactual Explanations using goal-directed ASP
Sopam Dasgupta, Joaquín Arias, Elmer Salazar +1
Machine learning models that automate decision-making are increasingly used in consequential areas such as loan approvals, pretrial bail approval, and hiring. Unfortunately, most o…
Counterfactual Generation with Answer Set Programming
Sopam Dasgupta, Farhad Shakerin, Joaquín Arias +2
Machine learning models that automate decision-making are increasingly being used in consequential areas such as loan approvals, pretrial bail approval, hiring, and many more. Unfo…
A Survey of the Various Methodologies Towards making Artificial Intelligence More Explainable
Sopam Dasgupta
Machines are being increasingly used in decision-making processes, resulting in the realization that decisions need explanations. Unfortunately, an increasing number of these deplo…
Counterfactual Explanation Generation with s(CASP)
Sopam Dasgupta, Farhad Shakerin, Joaquín Arias +2
Machine learning models that automate decision-making are increasingly being used in consequential areas such as loan approvals, pretrial bail, hiring, and many more. Unfortunately…