Bibliographic record
Abstract
As the demand grows to develop end-user trust in AI models, practitioners start to build and configure customized XAI (Explainable Artificial Intelligence) methods. The challenge is the lack of systematic evaluation of the newly proposed XAI method. As a result, it limits the confidence of XAI explanation in practice. In this paper, we follow a process of XAI method development and define two metrics in terms of consistency and efficiency in guiding the evaluation of trustworthy explanations. We demonstrate the development of a new XAI method in feature interactions called Mean-Centroid Preddiff, which analyzes and explains the feature importance order by a clustering algorithm. Following the process, we perform cross-validation on Mean-Centroid Preddiff with existing XAI methods. They show comparable consistency and gain in computation efficiency. The practice helps to adopt the core activities in the trustworthy evaluation of a new XAI method with rigorous cross-validation on consistency and efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.194 | 0.490 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".