Ethics in the Age of Algorithms: Unravelling the Impact of Algorithmic Unfairness on Data Analytics Recommendation Acceptance
Bibliographic record
Abstract
ABSTRACT Algorithms used in data analytics (DA) tools, particularly in high‐stakes contexts such as hiring and promotion, may yield unfair recommendations that deviate from merit‐based standards and adversely affect individuals. While significant research from fields such as machine learning and human–computer interaction (HCI) has advanced our understanding of algorithmic fairness, less is known about how managers in organisational contexts perceive and respond to unfair algorithmic recommendations, particularly in terms of individual‐level distributive fairness. This study focuses on job promotions to uncover how algorithmic unfairness impacts managers' perceived fairness and their subsequent acceptance of DA recommendations. Through an experimental study, we find that (1) algorithmic unfairness (against women) in promotion recommendations reduces managers' perceived distributive fairness, influencing their acceptance of these recommendations; (2) managers' trust in DA competency moderates the relationship between perceived fairness and DA recommendation acceptance; and (3) managers' moral identity moderates the impact of algorithmic unfairness on perceived fairness. These insights contribute to the existing literature by elucidating how perceived distributive fairness plays a critical role in managers' acceptance of unfair algorithmic outputs in job promotion contexts, highlighting the importance of trust and moral identity in these processes.
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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 | 0.031 | 0.151 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".