Fairness in Algorithmic Management: Bringing Platform-Workers into the Fold
Bibliographic record
Abstract
On digital labor platforms, algorithms execute a range of decisions including work assignments, performance evaluation, etc.Although algorithmic decision-making is a key feature of platform work, our understanding of how people perceive decisions made by algorithmsparticularly in terms of the fairness of their processes and outcomesremains underdeveloped.The impacts of such perceptions on job satisfaction and perceived organizational support (POS) are also still under exploration with some scholars challenging the possibility of POS among transient platform workers.In this paper, we explored the impacts of the perceived procedural and distributive fairness of algorithms operating in a paradigmatic context of algorithmic management, namely Uber.Drawing on the Theory of Organizational Justice, and a survey of 435 Uber drivers, we not only find that independent platform workers can experience POS, but that the fairness of managerial algorithms (in particular their outcomes) can play a critical role in stimulating such perceptions.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".