<scp>Human‐centered</scp> explainable artificial intelligence: An Annual Review of Information Science and Technology (ARIST) paper
Bibliographic record
Abstract
Abstract Explainability is central to trust and accountability in artificial intelligence (AI) applications. The field of human‐centered explainable AI (HCXAI) arose as a response to mainstream explainable AI (XAI) which was focused on algorithmic perspectives and technical challenges, and less on the needs and contexts of the non‐expert, lay user. HCXAI is characterized by putting humans at the center of AI explainability. Taking a sociotechnical perspective, HCXAI prioritizes user and situational contexts, preferences reflection over acquiescence, and promotes the actionability of explanations. This review identifies the foundational ideas of HCXAI, how those concepts are operationalized in system design, how legislation and regulations might normalize its objectives, and the challenges that HCXAI must address as it matures as a field.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".