Work

Generating Causally Compliant Counterfactual Explanations using ASP

Year: 2025

Type: article

Abstract: 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 approach generates (i) a counterfactual solution that represents a positive outcome and (ii) a path that will take us from the negative outcome to the positive one, where each node in the path represents a change in an attribute (feature) value. CoGS computes paths that respect the causal constraints among features. Thus, the counterfactuals computed by CoGS are realistic. CoGS utilizes rule-based machine learning algorithms to model causal dependencies between features. The paper discusses the current status of the research and the preliminary results obtained.

Source: Electronic Proceedings in Theoretical Computer Science

DOI: 10.4204/eptcs.416.30

Author: Sopam Dasgupta

Institution: The University of Texas at Dallas

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Science Foundation

Award: RI: SMALL: Inducing Answer Set Programs to Provide Accurate and Concise Explanation of Machine-learned Models