Principled Diverse Counterfactuals in Multilinear Models

Giannis Papantonis, Vaishak Belle

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)1421-1443
Number of pages23
JournalMachine Learning
Volume113
Issue number3
Early online date10 Jan 2024
DOIs
Publication statusPublished - 1 Mar 2024

Keywords / Materials (for Non-textual outputs)

  • XAI
  • counterfactuals
  • multilinear models

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