IMPACTS Homeostasis Trust Management System: Optimizing Trust in Human-AI Teams
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
Artificial Intelligence (AI) is becoming more ubiquitous throughout our lives. As our reliance on this technology increases, ensuring human operators maintain an adequate level of trust is integral to their safe and effective operations. To facilitate the appropriate level of operator trust in AI, a mechanism to continuously evaluate and calibrate human–AI trust is required. Such a Trust Management System (TMS) will be integral to developing trustworthy AI systems and thus enabling collaborative and effective Human–AI Teaming (HAT) in future operations. This article starts a review of the current state-of-the-art in trust research applicable to HAT, then summarizes the development and presents the IMPACTS (Intention, Measurability, Performance, Adaptivity, Communication, Transparency, Security) homeostasis TMS. It is based on a dynamic and transactional trust framework and allows for continuous trust monitoring, managing, and behaviour adjustment to ensure operator trust is calibrated.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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