Summary of Cfgs: Causality Constrained Counterfactual Explanations Using Goal-directed Asp, by Sopam Dasgupta et al.
CFGs: Causality Constrained Counterfactual Explanations using goal-directed ASP
by Sopam Dasgupta, Joaquín Arias, Elmer Salazar, Gopal Gupta
First submitted to arxiv on: 24 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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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, CFGs (CounterFactual Generation with s(CASP)), is a machine learning model that generates counterfactual explanations for automated decision-making systems. This framework uses a goal-directed Answer Set Programming (ASP) system, s(CASP), to automatically produce explanations from rule-based machine learning algorithms. The authors benchmark their proposal using the FOLD-SE model and demonstrate how CFGs navigates between different hypothetical scenarios, considering causal relationships among features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new approach to explaining decisions made by automated systems. It creates “counterfactual” examples that show why a decision was made, and how things could have been different if certain factors had changed. The authors develop a special computer program called s(CASP) that helps them generate these explanations. They test their idea using a specific type of machine learning model and show how it works. |
Keywords
» Artificial intelligence » Machine learning
