Merging Roles and Expertise: Redefining Stakeholder Characterization in Explainable Artificial Intelligence
Bibliographic record
Abstract
Explainable Artificial Intelligence (XAI) strives to make Artificial Intelligence Systems (AIS) more understandable, thus tackling the “black box” challenge. However, successful implementation requires precise identification of XAI requirements, made complex by the absence of universally accepted protocols. Given the importance of identifying stakeholders in this quest, this article proposes an innovative framework to characterize them. We compare and merge two predominant approaches: role-based and knowledge-based characterizations. The result is a novel framework, segmenting knowledge into subcategories while linking them to specific roles. This XAI Roles and Knowledge Framework offers a flexible methodology that can be adapted to the nuances of each XAI project. By providing a balance between specificity and generality, this tool aims to guide the implementation of XAI while ensuring that the stakeholders' needs are taken into account. By using this approach, XAI projects benefit from a more precise identification of needs, leading to outcomes more closely aligned with user expectations and greater transparency in AI decisions.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.009 | 0.027 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".