Me, Myself and AI: How Interacting with LLMs Affects Employees’ Expectations of AI at Work
Bibliographic record
Abstract
In two online studies (N = 484), we examine how one-on-one interactions with a Large Language Model (Open AI’s GPT-3) affect employees’ perceptions of AI’s abilities, attitudes toward AI, and willingness to work with AI in the future. To investigate this, we assigned employees four different work tasks: developing interview questions, fact checking, creating online content, and writing a recommendation letter, with instructions to rely on GPT-3 for each task. In Study 1, we find that while positive attitudes about AI increase and negative attitudes decrease post-interaction, the latter effect is more pronounced. Interestingly, we also show that people miscalibrate the impact of interacting with AI on their attitude changes. Our second study largely replicates Study 1 findings, and we additionally find that changes to task-specific perceptions (e.g., acceptability, performance, suitability, and willingness to use AI for a certain task) are highly dependent upon task types. Overall, we find that despite some initial qualms, people are more, rather than less, willing to work with a Large Language Model AI after having interacted with it in a work context. We discuss how these findings fit with current theorizing regarding the algorithm aversion-appreciation paradigm, as well as the applied implications.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".