mlsanityData to 7 Oct 2026

Comparables XAI: Faithful Example-based AI Explanations with Counterfactual Trace Adjustments

arXiv 2602.13784 ↗First posted 14 Feb 2026cs.HC cs.AIcited by 1

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Abstract (arXiv)

Explaining with examples is an intuitive way to justify AI decisions. However, it is challenging to understand how a decision value should change relative to the examples with many features differing by large amounts. We draw from real estate valuation that uses Comparables-examples with known values for comparison. Estimates are made more accurate by hypothetically adjusting the attributes of each Comparable and correspondingly changing the value based on factors. We propose Comparables XAI for relatable example-based explanations of AI with Trace adjustments that trace counterfactual changes from each Comparable to the Subject, one attribute at a time, monotonically along the AI feature space. In modelling and user studies, Trace-adjusted Comparables achieved the highest XAI faithfulness and precision, user accuracy, and narrowest uncertainty bounds compared to linear regression, linearly adjusted Comparables, or unadjusted Comparables. This work contributes a new analytical basis for using example-based explanations to improve user understanding of AI decisions.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 32 of its 117 references that are in the map. First posted on arXiv 14 Feb 2026.

Builds on

32 of 117 references.

References · 32

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20 more
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Citing papers per year: 2026: 1.

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  1. 2026SketchXplain: Intuitive Visual Explanations of Image Classifiers with Sketchescited by 0