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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": [
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"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"
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"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"}
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"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.",
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"annotation": "No human annotation. Synthetic-probe ground-truth intermediary variables (e.g., a*b for bilinear probe) are computed analytically from input features."
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