Summary of Sides: Separating Idealization From Deceptive Explanations in Xai, by Emily Sullivan
SIDEs: Separating Idealization from Deceptive Explanations in xAI
by Emily Sullivan
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitations of current Explainable AI (xAI) methods, which have been criticized for being manipulatable and potentially false. The authors argue that xAI research should engage in evaluating idealizations, intentional distortions introduced to scientific theories and models, to determine whether they are successful or deceptive. The study introduces a novel framework, SIDEs (Successful Idealization Detection), to assess the limitations of xAI methods and their potential for deception. The authors highlight the importance of idealization evaluation in xAI research, drawing parallels with the natural sciences and philosophy of science. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about improving how we explain artificial intelligence models so people can trust them. Currently, some AI explanations are seen as not being truthful or trustworthy. This paper proposes a new way to evaluate whether these explanations are successful or misleading. The authors believe that by understanding how these explanations work and what limitations they have, we can create better explanations that people will trust. |
