Perceived Factors of Trustworthiness in Generative Artificial Intelligence (GenAI): Towards an Understanding of how to Assess and Build Trustworthiness
Bibliographic record
Abstract
Given the benefits and risks associated with GenAI adoption in organizations, many academics and practitioners have stressed the importance of understanding how humans come to trust these technologies and the information and knowledge (e.g., solutions/decisions) they produce. The objective of this paper is to further examine human trust in AI technologies through the lens of a widely accepted organizational trust theory and model developed by Mayer, Davis, and Schoorman. More specifically, this paper focuses on developing a better understanding of perceived factors of GenAI trustworthiness since assessing trustworthiness is a critical determinant of trust. Building on the existing theory and model, it is proposed that an individual's perception of one or more of the following dimensions of trustworthiness - ability, integrity, and benevolence - will determine how trustworthy they find GenAI to be. Ability (or competence) refers to the trustee’s specific skills, knowledge, and expertise required in a specific domain. Integrity reflects the trustee’s sound values or principles (e.g., fairness, consistency, justice). Benevolence is an altruistic loyalty that reflects the trustee’s concern for the welfare, needs, desires, and interests of the individual over organizational or profit motives. Many researchers have proposed assessments related to GenAI ability, but integrity and benevolence are more difficult to assess, as technologies do not intrinsically embody human values or altruistic behaviors. Consequently, other parties within the organizations, such as AI designers and developers, strategic decision-makers, or the organization may be conflated into perceptions of these dimensions. The paper continues by briefly discussing how emotions and organizational culture may influence individuals' perceptions of trustworthiness and concludes by suggesting potential directions and strategies for building and representing each dimension of perceived trustworthiness in the context of GenAI.
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.
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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".