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| "rai:dataCollection": "Synthetic data is generated deterministically from known mathematical functions with fixed random seeds. Real-world datasets are sourced from scikit-learn built-in datasets and OpenML public repositories. No human subjects data is collected.", |
| "rai:dataCollectionType": "Synthetic generation + public dataset aggregation", |
| "rai:dataCollectionMissingness": "One real-world dataset (Concrete) was excluded due to an OpenML cache failure. All other datasets loaded successfully.", |
| "rai:dataBiases": "Synthetic probes use continuous numeric features only; categorical features and missing values are not tested in the core benchmark. Real-world datasets inherit biases from their original sources (e.g., California Housing reflects historical housing patterns). The benchmark evaluates MI methods, not model fairness.", |
| "rai:dataLimitations": [ |
| "Core experiments use small scale (N_train=100, N_test=50)", |
| "Only regression synthetic probes; classification probes are secondary", |
| "5 models from 3 architectural families; generalization to future TFMs is not guaranteed", |
| "Computation profiles are descriptive reference baselines, not universal laws", |
| "Benchmark outputs are diagnostic measurements, not deployment certifications" |
| ], |
| "rai:dataUseCases": "Evaluating new MI techniques on TFMs, benchmarking new TFM architectures against MI reference baselines, educational resource for understanding TFM internal computation.", |
| "rai:dataSensitiveInformation": "No personally identifiable information (PII). No sensitive attributes beyond those in the original public datasets (e.g., Adult Income contains demographic attributes).", |
| "rai:personalSensitiveInformation": "No personally identifiable information (PII) is collected, generated, or distributed. Synthetic probes contain no human-subjects data. Real-world datasets are loaded at runtime from public repositories (scikit-learn, OpenML) and inherit only the demographic attributes already public in those sources (e.g., Adult Income age/sex/race columns); these are NOT augmented or transformed by TabMI-Bench. The benchmark itself does not perform any inference on individuals; it analyses model internal activations on tabular inputs.", |
| "rai:dataSocialImpact": "Positive: enables standardized MI evaluation for tabular AI in high-stakes domains (credit scoring, medical diagnosis). Risk: benchmark outputs may create false confidence if over-interpreted as safety certifications. Mitigation: explicit scope limitations and risk caveats in the paper.", |
| "rai:dataSyntheticGeneration": "Core synthetic probes are fully synthetic with known ground-truth intermediary variables. Real-world datasets are not synthetic.", |
| "rai:hasSyntheticData": true, |
| "rai:dataMaintenancePlan": "Version-pinned snapshots ensure reproducibility. New model hooks and reference baselines will be added as TFMs are released. Community contributions welcomed via pull requests.", |
| "rai:dataSourceDatasets": [ |
| {"name": "California Housing", "url": "https://www.openml.org/d/8092", "license": "CC0", "purpose": "real-world causal tracing + steering validation"}, |
| {"name": "Diabetes (sklearn)", "url": "https://scikit-learn.org/stable/datasets/toy_dataset.html", "license": "BSD-3-Clause", "purpose": "real-world causal tracing + steering validation"}, |
| {"name": "Wine Quality", "url": "https://www.openml.org/d/287", "license": "CC0", "purpose": "real-world steering"}, |
| {"name": "Bike Sharing", "url": "https://www.openml.org/d/44063", "license": "CC0", "purpose": "real-world steering"}, |
| {"name": "Abalone", "url": "https://www.openml.org/d/183", "license": "CC0", "purpose": "real-world causal tracing"}, |
| {"name": "Boston Housing", "url": "https://www.openml.org/d/531", "license": "CC0", "purpose": "real-world causal tracing"}, |
| {"name": "Energy Efficiency", "url": "https://www.openml.org/d/42178", "license": "CC0", "purpose": "real-world causal tracing"}, |
| {"name": "Breast Cancer (sklearn)", "url": "https://scikit-learn.org/stable/datasets/toy_dataset.html", "license": "BSD-3-Clause", "purpose": "real-world causal tracing + classification probing"}, |
| {"name": "Iris (sklearn)", "url": "https://scikit-learn.org/stable/datasets/toy_dataset.html", "license": "BSD-3-Clause", "purpose": "classification probing"}, |
| {"name": "Adult Income", "url": "https://www.openml.org/d/1590", "license": "CC0", "purpose": "real-world causal tracing"}, |
| {"name": "Credit-G", "url": "https://www.openml.org/d/31", "license": "CC0", "purpose": "real-world causal tracing"} |
| ], |
| "rai:dataProvenance": { |
| "collection": "All real-world datasets are loaded at runtime from public repositories (scikit-learn built-in datasets, OpenML.org); no new human-subjects data collection performed.", |
| "preprocessing": "Real-world: StandardScaler applied with fixed random seed for train/test split; categorical features encoded via OpenML's default encoders. Synthetic: features generated via numpy.random.default_rng(seed) and labels computed from closed-form mathematical functions with optional Gaussian noise.", |
| "annotation": "No human annotation. Synthetic-probe ground-truth intermediary variables (e.g., a*b for bilinear probe) are computed analytically from input features." |
| } |
| } |
|
|
