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Conference Papers Year : 2024

A Maximin Approach to Elicit Gödel Integral in an XAI Context

Abstract

The flexibility of the Gödel integral as aggregation tool comes from its parameter, defined as a capacity. A crucial question is that of the choice of this parameter. This paper considers the case when two criteria are to be aggregated and provides an elicitation protocol as an incremental algorithm called INES which stands for Interactive Necessary Example Search. It allows to reduce progressively the uncertainty about the capacity, until it is possible to identify a "necessary winner": the latter is an alternative that maximises the Gödel integral value for all instantiations induced by the current capacity candidates. The proposed INES algorithm is illustrated in an explainable AI (XAI) context: it is used to find the characteristics of the best counterfactual candidate, in terms of objective and subjective evaluations, even if the user requirements about their aggregation is still not completely known.
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Dates and versions

hal-04572334 , version 1 (10-05-2024)

Identifiers

  • HAL Id : hal-04572334 , version 1

Cite

Agnès Rico, Marie-Jeanne Lesot, Christophe Marsala. A Maximin Approach to Elicit Gödel Integral in an XAI Context. IEEE World Congress on Computational Intelligence (IEEE WCCI 2024 / Fuzz-IEEE 2024), Jun 2024, Yokohama, Japan. ⟨hal-04572334⟩
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