Accepting the unacceptable in the AI era: When & how AI recommendations drive unethical decisions in organizations
Bibliographic record
Abstract
In today’s workplaces, the promise of AI recommendations must be balanced against possible risks. We conducted an experiment to better understand when and how ethical concerns could arise. In total, 379 managers made either one or multiple organizational decisions with input from a human or AI source. We found that, when making multiple, simultaneous decisions, managers who received AI recommendations were more likely to exhibit lowered moral awareness, meaning reduced recognition of a situation’s moral or ethical implications, compared with those receiving human guidance. This tendency did not occur when making a single decision. In supplemental experiments, we found that receiving AI recommendations on multiple decisions increased the likelihood of making a less ethical choice. These findings highlight the importance of developing organizational policies that mitigate ethical risks posed by using AI in decision-making. Such policies could, for example, nudge employees toward recalling ethical guidelines or reduce the volume of decisions that are made simultaneously.
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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 | 0.021 | 0.140 |
| 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 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".