Dumas, Bruno

Maxwell, Winston

English

This report explores the concept of explainability in AI-based systems, distinguishing between "local" and "global" explanations. “Local” explanations refer to specific algorithmic outputs in their operational context, while “global” explanations encompass the system as a whole. The need to tailor explanations to users and tasks is emphasised, acknowledging that explanations are not universal solutions and can have unintended consequences. Two use cases illustrate the application of explainability techniques: an educational recommender system, and explainable AI for scientific discoveries. The report discusses the subjective nature of meaningfulness in explanations and proposes cognitive metrics for its evaluation. It concludes by providing recommendations, including the inclusion of “local” explainability guidelines in the EU AI proposal, the adoption of a user-centric design methodology, and the harmonisation of explainable AI requirements across different EU legislation and case law.Overall, this report delves into the framework and use cases surrounding explainability in AI-based systems, emphasising the need for “local” and “global” explanations, and ensuring they are tailored toward users of AI-based systems and their tasks

Repository of the University of Namur

Meaningful XAI Based on User-Centric Design Methodology

https://pure.unamur.be/ws/files/87934339/CERRE-Report-Meaningful-XIA-10072023.pdf

Meaningful XAI Based on User-Centric Design Methodology

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Repository of the University of Namur