Do algorithms play fair? Analysing the perceived fairness of HR-decisions made by algorithms and their impacts on gig-workers
Bibliographic record
Abstract
On digital labour platforms, algorithms execute a wide range of human resource (HR) decisions including work allocation and performance evaluation. Despite their growing use, our understanding of how people perceive such algorithms, particularly in terms of fairness, is less developed. Using Organisational Justice Theory, we explore how workers perceive the fairness of HR-decisions made by algorithms and how those perceptions impact job satisfaction and perceived organisational support (POS). Results from a survey of 435 Uber drivers indicate that perceptions of algorithmic fairness – and their formation – differ based on the type of HR-decision enacted by an algorithm and whether those decisions are considered to require mechanical or human skills. Results also demonstrate positive significant relationships between perceived algorithmic fairness, POS, and job satisfaction. This study answers calls to investigate perceptions of algorithmic fairness across different HR-decisions and their impacts in real-world settings. Our results suggest that algorithms play an important role in shaping platform-workers’ experiences and attitudes as both technological artefacts and social agents of the organisation. Recommendations for improving the perceived fairness of algorithms for HR-decisions by focusing on transparency and high impact/value fairness indicators are offered.
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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 | 0.011 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".