The dark side of artificial intelligence in services
Bibliographic record
Abstract
Artificial intelligence (AI) initiatives, including Generative AI, are being increasingly implemented in service industries, and are having a great impact on service operations and on customers’ reactions and behaviors. Previous literature is overoptimistic about AI implementation, and there is still a need to explore the dark side of this technology; that is, its potential negative impacts on consumers, businesses, and society, as well as the moral concerns associated with AI use in services. To establish some fundamental insights related to this research domain, this paper contributes to previous AI based-services literature by proposing a three-part conceptual model inspired by Belanche et al. (2020a), comprised of AI design, customers, and the service encounter. Specifically, we identify key factors and research gaps within each category that need to be addressed. The final research questions provide a research agenda to guide scholars and help practitioners implement AI-based services while avoiding their potential negative outcomes.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".