Ethics in the Age of Algorithms: Unravelling the Impact of Algorithmic Unfairness on Data Analytics Recommendation Acceptance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it