Trustworthy AI: AI developers’ lens to implementation challenges and opportunities
Bibliographic record
Abstract
As organizations continue to embrace the use of artificial intelligence (AI) systems, it is crucial to ensure that these AI systems can be trusted. However, there is still a significant gap between research on trustworthy AI and its implementation in real-world applications. To address this issue, we sought to explore the perspectives of AI developers and the challenges they face in creating trustworthy AI systems. This exploratory study involved conducting interviews with 19 AI developers. We identified key challenges faced by AI developers due to the immature state of trustworthy AI, inconsistent global regulatory landscape, a lack of standardized definitions of key concepts, limited tools and standards for practical implementation in organizations. This paper provides recommendations for organizations to invest in trustworthy AI processes and practices, this includes building a foundation for trustworthy AI specific to their organization, adopting an organizational approach to trustworthy AI culture, and providing proper data infrastructures to support AI developers in creating trustworthy AI systems. By investing in trustworthy AI practices, organizations can prepare for evolving regulations and ensure that their AI systems are reliable and trustworthy.
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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 | 0.066 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.014 | 0.071 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".