Responsible AI measures dataset for ethics evaluation of AI systems
Bibliographic record
Abstract
Meaningful governance of any system requires the system to be assessed and monitored effectively. In the domain of Artificial Intelligence (AI), global efforts have established a set of ethical principles, including fairness, transparency, and privacy upon which AI governance expectations are being built. The computing research community has proposed numerous means of measuring an AI system's normative qualities along these principles. Current reporting of these measures is principle-specific, limited in scope, or otherwise dispersed across publication platforms, hindering the domain's ability to critique its practices. To address this, we introduce the Responsible AI Measures Dataset, consolidating 12,067 data points across 791 evaluation measures covering 11 ethical principles. It is extracted from a corpus of computing literature (n = 257) published between 2011 and 2023. The dataset includes detailed descriptions of each measure, AI system characteristics, and publication metadata. An accompanying, interactive visualization tool supports usability and interpretation of the dataset. The Responsible AI Measures Dataset enables practitioners to explore existing assessment approaches and critically analyze how the computing domain measures normative concepts.
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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 | 0.008 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".