Counterfactual Generation with Answer Set Programming
<|reference_start|>Counterfactual Generation with Answer Set Programming: 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. Unfortunately, most of these models are black-boxes, i.e., they are unable to reveal how they reach these prediction decisions. A need for transparency demands justification for such predictions. An affected individual might also desire explanations to understand why a decision was made. Ethical and legal considerations may further require informing the individual of changes in the input attribute that could be made to produce a desirable outcome. This paper focuses on the latter problem of automatically generating counterfactual explanations. We propose a framework Counterfactual Generation with s(CASP) (CFGS) that utilizes answer set programming (ASP) and the s(CASP) goal-directed ASP system to automatically generate counterfactual explanations from rules generated by rule-based machine learning (RBML) algorithms. In our framework, we show how counterfactual explanations are computed and justified by imagining worlds where some or all factual assumptions are altered/changed. More importantly, we show how we can navigate between these worlds, namely, go from our original world/scenario where we obtain an undesired outcome to the imagined world/scenario where we obtain a desired/favourable outcome.<|reference_end|>
@article{dasgupta2024counterfactual,
    title={Counterfactual Generation with Answer Set Programming},
    author={Sopam Dasgupta, Farhad Shakerin, Joaqu'in Arias, Elmer Salazar, Gopal
  Gupta},
    journal={arXiv preprint arXiv:2402.04382},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04382},
    primaryClass={cs.AI}
}