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"How Good Is Your Explanation?": Towards a Standardised Evaluation Approach for Diverse XAI Methods on Multiple Dimensions of Explainability

Published: 28 June 2024 Publication History

Abstract

Artificial Intelligence (AI) systems involve diverse components, such as data, models, users and predicted outcomes. To elucidate these different aspects of AI systems, multifaceted explanations that combine diverse explainable AI (XAI) methods are beneficial. However, popularly adopted user-centric XAI evaluation methods do not measure these explanations across the different components of the system. In this position paper, we advocate for an approach tailored to evaluate XAI methods considering the diverse dimensions of explainability within AI systems using a normalised scale. We argue that the prevalent user-centric evaluation methods fall short of facilitating meaningful comparisons across different types of XAI methodologies. Moreover, we discuss the potential advantages of adopting a standardised approach, which would enable comprehensive evaluations of explainability across systems. By considering various dimensions of explainability, such as data, model, predictions, and target users, a standardised evaluation approach promises to facilitate both inter-system and intra-system comparisons for user-centric AI systems.

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          cover image ACM Conferences
          UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
          June 2024
          662 pages
          ISBN:9798400704666
          DOI:10.1145/3631700
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          Published: 28 June 2024

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          1. Explainable AI
          2. Explainable AI Evaluation
          3. XAI

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