Research code, evaluation frameworks, and datasets published to enable scrutiny, reproducibility, and independent review. These resources support AI governance and safety evaluation, not autonomous deployment.
Reference implementations and evaluation frameworks for AI safety analysis and governance review
Reference inference framework demonstrating safety controls including guardrails, output filtering, and decision logging for audit discussions.
Interactive decision support tool for analyzing cost, quality, and latency tradeoffs during AI system planning and governance review.
Benchmarking toolkit for evaluating bias and disparate impact across protected attributes and use cases in pre-deployment review.
Unified interpretability toolkit providing SHAP, LIME, and attention visualization methods for explainability analysis and audit readiness.
Document integrity evaluation toolkit combining cryptographic hashing and ML based tamper detection for forensic review and compliance workflows.
Reference implementations for uncertainty estimation and calibration analysis, including conformal prediction, ensembles, and Bayesian methods.
Datasets published to support evaluation, benchmarking, and reproducible research
Bias evaluation dataset for NLP tasks across multiple protected categories. 250,000 annotated samples for research and evaluation.
Document integrity and tamper detection benchmark containing 100,000 pristine and tampered documents for evaluation and benchmarking.
Healthcare AI safety evaluation dataset with 15,000 expert physician annotations for safety evaluation and governance review.
Multi-domain benchmark with 500,000 samples for uncertainty calibration and confidence assessment across evaluation and methodological research.