Decoding Ethical Affordances in HR Algorithms Through an Actor-Network Theory Perspective
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
The rise and proliferation of Human Resources (HR) Algorithms brought attention to ethical questions on the application of the technology. However, the implementation of such technology in HR is viewed as conscious, deliberate, and intentional —-- fully under the control of the human actors. While such theoretical perspective allows the scholarship to investigate ethical concerns stemming from the social factors (designers or users of the IT solutions,) the features built into the technology (i.e. affordances or action possibilities of a technical object for human) are largely ignored. We suggest that the HR field can benefit from applying Actor-Network Theory to analyze the formation and reformation processes that involve all human and non-human actors in the network. We also argue that by applying the new theoretical framework, a “drift” in ethical value can occur during the translation processes, resulting in unintended outcomes. We identify four different kinds of ethical affordances of HR Algorithms that could aggravate such “drift” and suggest that researchers and regulators should contribute to the setting up of guidelines to build human value into the networks.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Science and technology studies Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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