Explain it to me like I’m five: harnessing the power of explanations to increase trust in workplace generative AI
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
The rise of intelligent machines is set to revolutionize the way that employees work. Organizations are investing in these technologies at unprecedented rates, and this investment will permanently reshape the world of work. As employees learn to work alongside technologies with never-before-seen capabilities, it is imperative to better understand what will enable employees to extend trust to artificial intelligence (AI). The current research investigates whether providing explanations prior to using an AI tool increases trust in intelligent workplace technologies. We leverage the ‘how’ and ‘why’ explanation paradigm, in which the ‘how’ information describes the process underlying the technology and the ‘why’ information describes the benefits of using the technology. We conducted an experiment using a simulated AI marketing application with a sample of working professionals (N = 303). We found that trust increased when participants received an explanation of ‘why’ using the technology would be beneficial. We conclude that explanations are a viable avenue to enhance trust in AI in workplace settings. The theoretical and practical implications of these findings are discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it