A Human-in-the-loop Workflow for Multi-Factorial Sensitivity Analysis of Algorithmic Rankers
Bibliographic record
Abstract
Algorithmic rankers are ubiquitously applied in automated decision systems such as hiring, admission, and loan-approval systems. Without appropriate explanations, decision-makers often cannot audit or trust algorithmic rankers' outcomes. In recent years, XAI (explainable AI) methods have focused on classification models, but there for algorithmic rankers, we are yet to develop state-of-the-art explanation methods. Moreover, explanations are also sensitive to changes in data and ranker properties, and decision-makers need transparent model diagnostics for calibrating the degree and impact of ranker sensitivity. To fulfill these needs, we take a dual approach of: i) designing explanations by transforming Shapley values for the simple form of a ranker based on linear weighted summation and ii) designing a human-in-the-loop sensitivity analysis workflow by simulating data whose attributes follow user-specified statistical distributions and correlations. We leverage a visualization interface to validate the transformed Shapley values and draw inferences from them by leveraging multi-factorial simulations, including data distributions, ranker parameters, and rank ranges.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".