Framing the Human-Centered Artificial Intelligence Concepts and Methods: Scoping Review
Bibliographic record
Abstract
Background: With the rapid expansion of artificial intelligence (AI) applications, researchers have begun focusing on the concept of human-centered artificial intelligence (HCAI). This field is dedicated to designing AI systems that augment and improve human abilities, rather than substituting them. Objective: The objective of the paper was to review the information on design principles, techniques, applications, methods, and outcomes adopted in the field of HCAI, in order to provide some insights on the discipline, in relation with the broader concepts of human-centered and user-centered design. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist guidelines, we conducted a scoping review in PubMed, ScienceDirect, and IEEE Xplore, including all study types, excluding narrative reviews and editorials. Results: Out of the 1035 studies retrieved, 14 studies conducted between 2018 and 2023 met the inclusion criteria. The main fields of application were the health sector and AI applications. Human-centered design methodologies were adopted in 3 studies, personas in 2 studies, while the remaining methodologies were adopted in individual studies. Conclusions: HCAI emphasizes designing AI systems that prioritize human needs, satisfaction, and trustworthiness, but current principles and guidelines are often vague and difficult to implement. The review highlights the importance of involving users early in the development process to enhance trust, especially in fields like health care, but notes that there is a lack of standardized HCAI methodologies and limited practical applications adhering to these principles.
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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 | 0.171 | 0.424 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.053 | 0.037 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".