Landscape of User-Centered Design Practices for Fostering Trustworthy Human-AI Interactions
Bibliographic record
Abstract
In the advent of the fourth industrial revolution, leaders in the commercial artificial intelligence (AI) market have shaped parameters of trustworthy AI design. Resulting directives, however, typically approach this issue from a technical perspective, while largely ignoring its human factors counterpart. As such, we conducted an information synthesis to capture the current landscape of user-centered design for developing trustworthy human-AI interactions. As part of our review, we analyzed resources from 50+ publications and summarized their respective protocols into three major categories: (1) design standards and guidelines based on ethical principles, 2) best practices for designing the nature of human-AI relationships across the user experience, and 3) best practices for designing redress mechanisms when trust is at risk, low, or broken. Part and parcel to this review, we provide concrete prescriptions for designing trustworthy human-AI interactions, identify limitations of existing protocols, and suggest areas requiring further exploration.
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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 | 0.175 | 0.194 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".