Explanation seeking and anomalous recommendation adherence in human‐to‐human versus human‐to‐artificial intelligence interactions
Bibliographic record
Abstract
Abstract The use of artificial intelligence (AI) in operational decision‐making is growing, but individuals can display algorithm aversion, preventing adherence to AI system recommendations—even when the system outperforms human decision‐makers. Understanding why such algorithm aversion occurs and how to reduce it is important to ensure AI is fully leveraged. While the ability to seek an explanation from an AI may be a promising approach to mitigate this aversion, there is conflicting evidence on their benefits. Based on several behavioral theories, including Bayesian choice, loss aversion, and sunk cost avoidance, we hypothesize that if a recommendation is perceived as an anomalous loss, it will decrease recommendation adherence; however, the effect will be mediated by explanations and differ depending on whether the advisor providing the recommendation and explanation is a human or an AI. We conducted a survey‐based lab experiment set in the online rental market space and found that presenting a recommendation as a loss anomaly significantly reduces adherence compared to presenting it as a gain, however, this negative effect can be dampened if the advisor is an AI. We find explanation‐seeking has a limited impact on adherence, even after considering the influence of the advisor; we discuss the managerial and theoretical implications of these findings.
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 | 0.008 | 0.089 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".