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A Review of the Convergence Between Explainable Artificial Intelligence and Multi-Objective Optimization

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posted on 2022-12-12, 20:46 authored by Wellington Rodrigo MonteiroWellington Rodrigo Monteiro, Gilberto Reynoso-Meza

Explainable artificial intelligence (XAI) is a new recent area that encompasses techniques attempting to better explain to humans how a given trained machine learning (ML) model work ensuring they can trust, understand and appropriately manage the model. On the other hand, multi-objective optimization (MOO) includes a series of algorithms that attempt to minimize or maximize, at the same time, two or more discordant objectives. One of XAI's current challenges is balancing accuracy and human interpretability -- a tradeoff of two conflicting goals. Therefore, the adoption of MOO techniques within XAI might be suitable. Surprisingly, there is a minimal amount of literature available addressing both areas. This document proposes a systematic literature review to identify the primary research in both fields.


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Submitting Author's Institution

Pontifical Catholic University of Parana

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  • Brazil

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