Exploring the Role of Trust during Human-AI Collaboration in Managerial Decision-Making Processes
Bibliographic record
Abstract
Despite the growing popularity of using Artificial Intelligence-based (AI-based) models to assist human decision-makers, little is known about how managers in business environments approach AI-assisted decision-making.Thus, our research is guided by two questions: (1) What facets make the Human (Manager)-AI decision-making process trustworthy, and (2) Does trust in AI depend on the degree to which the AI agent is humanized?Our results show that (a) AI is preferred for operational versus strategic decisions and decisions that indirectly affect individuals, (b) the ability to interpret the decision-making process of AI agents would help improve user trust and alleviate calibration bias, (c) humanoid interaction styles were believed to improve the interpretability of the decision-making process, and (d) organizational change management was essential for adopting AI technologies.Our survey analysis indicates that when interpretability and model confidence are present in the decision-making process involving an AI agent, higher trustworthiness scores are observed.
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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 | 0.018 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".