What’s Individual About Individual Fairness?
Bibliographic record
Abstract
Individual and group fairness notions abound in the machine learning literature. Each attempts to formalize harm against individuals or groups of people. In this work, we take a step back and aim to characterize, from a learning theory perspective, what is at the heart of individual fairness (IF) notions. We argue that fairness notions should be comparison-based and, in the case of IF notions, that any failure to be fair should give rise to finite evidence of unfairness. We also posit that IF notions should have an unfairness ``direction'', for example via an order on the set of potential decisions. Equipped with this framework, we present various ways unfair classifiers can be compared to each other. Comparing classifiers is essential in any situation where there is a need to choose between not-perfectly-fair classifiers, e.g., in cases where there exist unavoidable trade-offs between learning objectives. We then adapt score-based measures of individual unfairness to allow us to measure how harm is distributed between population subgroups, which is more in line with group fairness. Crucially, our set-up retains evidence of harm at the individual level, allowing for algorithmic recourse, or potential integrations within legal frameworks.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".