Explanations match real reasons when tests stay simple and clear
A simple test checks if explanations point to real reasons even when the model's details are hidden, showing limits tied to data and masking.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Bhanu Tokas, Rahul Nair, Rockwell Jackson, Hannah Kerner
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
Researchers tested a method that finds a picture concept, marks the part of the image that shows it, hides that part, and checks how the model's answers change to see if the explanation points to real causes.
Why it matters
This helps users see when AI relies on obvious cues and where explanations can mislead, guiding careful use and checks before decisions.
When cues are clear and the image group fits the concept, explanations align with real causes, giving a practical view of how the model makes choices.
In real use, mixed image sets and poorly separated ideas can blur results and weaken the trustworthiness of explanations for practitioners who rely on them.
What remains uncertain
Explanations rely on clearly separated ideas and on the quality of the image set used to mask parts in tests.
Original sources · 1
- Hide and Seek: HIDE Concepts to SEEK explanations for black box models ↗arXiv · 2026-10-09
Check the original paper for its authors, methods, version and access terms.
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