Responsible artificial intelligence in human resources management: a review of the empirical literature
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 As it is the case for many business processes and activities disciplines, artificial intelligence (AI) is increasingly integrated in human resources management (HRM). While AI has great potential to augment the HRM activities in organizations, automating the management of humans is not without risks and limitations. The identification of these risks is fundamental to promote responsible use of AI in HRM. We thus conducted a review of the empirical academic literature across disciplines on the affordances and responsible principles of AI in HRM. This is the first review of responsible AI in HRM that focuses solely on studies containing observations, measurements, and tests about this phenomenon. The multi-domain and multidisciplinary approach and empirical focus provides a better understanding of the reality of the development, study, and deployment of AI in HRM and sheds light on how these are conducted responsibly. We conclude with a call for research based on what we identified as the most needed and promising avenues.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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