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[2602.13784] Comparables XAI: Faithful Example-based AI Explanations with Counterfactual Trace Adjustments
Summary
The paper introduces Comparables XAI, a method for providing faithful, example-based AI explanations using counterfactual trace adjustments to enhance user understanding of AI decisions.
Why It Matters
As AI systems become more prevalent, understanding their decision-making processes is crucial for trust and accountability. Comparables XAI offers a novel approach to improve the interpretability of AI models, making it easier for users to grasp how decisions are made based on example comparisons.
Key Takeaways
- Comparables XAI enhances AI explanations by using relatable examples.
- The method employs counterfactual trace adjustments for improved accuracy.
- User studies show Comparables XAI achieves higher faithfulness and precision than traditional methods.
- This approach can lead to better user understanding of AI decisions.
- The research contributes to the field of Human-Computer Interaction and AI transparency.
Computer Science > Human-Computer Interaction arXiv:2602.13784 (cs) [Submitted on 14 Feb 2026] Title:Comparables XAI: Faithful Example-based AI Explanations with Counterfactual Trace Adjustments Authors:Yifan Zhang, Tianle Ren, Fei Wang, Brian Y Lim View a PDF of the paper titled Comparables XAI: Faithful Example-based AI Explanations with Counterfactual Trace Adjustments, by Yifan Zhang and 3 other authors View PDF HTML (experimental) Abstract: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 us...
