The paper discusses several foundational issues in the field of explainable artificial intelligence (XAI), including the lack of proper definitions for explanations, the absence of theoretical guarantees, the difficulty in defining simple quantitative evaluation metrics, and the lack of uncertainty quantification for explanations.
To address these challenges, the author proposes leveraging standard statistical tools and techniques. Specifically:
Defining explanations as statistical quantities, such as variable importance measures, which can be precisely formulated and estimated using statistical estimators. This provides a clear mathematical definition of explanations.
Establishing theoretical guarantees for the explanations by proving convergence results as the amount of data increases, using tools like the law of large numbers and central limit theorem.
Defining quantitative evaluation metrics based on the statistical definition of explanations, enabling objective assessment of the quality of explanations without relying on subjective human evaluations.
Incorporating uncertainty quantification for the explanations through classical statistical procedures like the bootstrap, providing insights into the robustness and variability of the explanations.
The author also discusses additional benefits of the statistical approach, such as enabling trustworthy explanations, the ability to use interpretable statistical models, and the potential for assessing fairness. However, the author acknowledges that some challenges, like defining the purpose of explanations and ensuring their simplicity, cannot be fully resolved by statistics alone.
Overall, the paper advocates for a closer integration of statistical methods and XAI techniques to address fundamental issues in the field of explainability.
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by Valentina Gh... في arxiv.org 05-01-2024
https://arxiv.org/pdf/2404.19301.pdfاستفسارات أعمق