Generating Causally Compliant Counterfactual Explanations using ASP
Fuente:
arXiv
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866915149727662080 |
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| author | Dasgupta, Sopam |
| author_facet | Dasgupta, Sopam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09226 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generating Causally Compliant Counterfactual Explanations using ASP Dasgupta, Sopam Artificial Intelligence 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. |
| title | Generating Causally Compliant Counterfactual Explanations using ASP |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2502.09226 |