The paper advocates for a nuanced understanding of counterfactual explanations (CFEs) in the field of Explainable Artificial Intelligence (XAI). It recognizes that the desired properties of CFEs can vary significantly depending on the user's objectives and target applications.
The authors identify three primary user objectives for CFEs:
Outcome Fulfillment: The user seeks advice on how to modify the input to an AI system to achieve a desired output. In this case, both actionability (modifying only mutable features) and plausibility (modifying features in a reasonable way) are desired properties.
System Investigation: The user aims to understand the behavior of the AI system, uncover potential biases, or reveal inconsistencies. Plausibility of the counterfactual instances is important, but actionability is not a strict requirement, as investigating immutable features can provide valuable insights.
Vulnerability Detection: The user seeks to identify potential weaknesses or vulnerabilities in the AI system. In this case, considerations of plausibility and actionability may pose conflicts with the user's objectives, as they could impede the detection of vulnerabilities to attacks involving random noise or out-of-distribution permutations.
The paper emphasizes the need for customized explanations that address the specific requirements of users across diverse scenarios, rather than a one-size-fits-all approach. It highlights the limitations of a unified strategy for CFEs and calls for further exploration of the nuances of CFEs and the development of methodologies for tailoring explanations to meet the evolving needs of users.
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Önemli Bilgiler Şuradan Elde Edildi
by Orfeas Menis... : arxiv.org 04-16-2024
https://arxiv.org/pdf/2404.08721.pdfDaha Derin Sorular