Principled Diverse Counterfactuals in Multilinear Models
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Abstract (arXiv)
Machine learning (ML) applications have automated numerous real-life tasks, improving both private and public life. However, the black-box nature of many state-of-the-art models poses the challenge of model verification; how can one be sure that the algorithm bases its decisions on the proper criteria, or that it does not discriminate against certain minority groups? In this paper we propose a way to generate diverse counterfactual explanations from multilinear models, a broad class which includes Random Forests, as well as Bayesian Networks.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 51 references that are in the map. First posted on arXiv 17 Jan 2022.
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20 of 51 references.
References · 20
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8 more
- 2020Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Oddscited by 40
- 2014Interpreting Tree Ensembles with inTreescited by 37
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