<scp>Human‐centered</scp> explainable artificial intelligence: An Annual Review of Information Science and Technology (ARIST) paper
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.025 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it