Checks and balances: leveraging artificial intelligence for tri-balance personnel selection systems
Bibliographic record
Abstract
The integration of algorithmic tools in human resource management (HRM) presents both significant opportunities and pressing challenges, particularly in personnel selection, where the adoption of artificial intelligence (AI) tools is rapidly expanding. However, academic research has lagged behind, leaving theoretical and empirical gaps in understanding the implications of AI-driven selection systems. This study employs an interdisciplinary approach, drawing from industrial relations and social psychology, to examine the integration of AI in staffing practices. Through qualitative analysis of 30 stakeholder interviews, it develops an inductive theory identifying efficiency, equity, and voice as core objectives shaping effective and responsible AI-based selection systems. The findings reveal that balancing these objectives requires navigating complex internal and external dynamics, culminating in the proposed tri-balance model. This model offers a framework for aligning stakeholder priorities and optimizing AI adoption. The study concludes by discussing its theoretical contributions, practical implications, and avenues for future research on algorithmic personnel selection.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".