Summary of Counterfactual Generation with Answer Set Programming, by Sopam Dasgupta et al.
Counterfactual Generation with Answer Set Programming
by Sopam Dasgupta, Farhad Shakerin, Joaquín Arias, Elmer Salazar, Gopal Gupta
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, Counterfactual Generation with s(CASP) (CFGS), uses answer set programming (ASP) and goal-directed ASP system to generate counterfactual explanations from rules generated by rule-based machine learning (RBML) algorithms. This allows for the computation and justification of explanations that show how decisions could be changed to produce a desired outcome, addressing ethical and legal considerations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a way to explain why decisions were made in machine learning models used in important areas like loan approvals, hiring, and more. The goal is to help people understand how decisions are reached and what changes could be made to get a better result. This is done by imagining different scenarios where some assumptions are changed, showing how we can go from an undesired outcome to a desired one. |
Keywords
* Artificial intelligence * Machine learning
