Information Resilient Society in an <scp>AI</scp> World—Is <scp>XAI</scp> Sufficient?
Bibliographic record
Abstract
ABSTRACT This paper discusses the role of organizational records management (ORM) with respect to the field of explainable artificial intelligence (XAI) and argues about its necessity and significance. The current trend is to utilize AI to explain AI, including both explanation production and provision. We argue that this kind of approach is not sufficient by itself as it lacks neutrality and localness to explanation recipients, thus ineffective in establishing public trust. We propose the addition of the ORM profession, as an informational 3rd party, to the current and future XAI. We envision that, by working together with XAI performers, ORM contributes to an information resilient AI society. To take on the related responsibilities, the ORM profession must improve its professional expertise and strengthen its professional independence. We call for consensus among individual information professionals and the advocacy of information professional networks to make this vision a reality.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".