We believe transparency is a prerequisite for trustworthy AI. Our open-source research code, evaluation tools, and datasets are published to enable scrutiny, reproducibility, and independent review.
These resources support AI governance, safety evaluation, and risk assessment, not autonomous deployment.
The open-source tools and datasets listed on this page are provided for research, evaluation, and governance support.
They are not production systems, do not constitute deployment approval, and are not substitutes for independent AI risk audits or regulatory review.
Reference implementations and evaluation frameworks used to support AI safety analysis and governance review
Reference inference framework designed to demonstrate safety controls, including guardrails, output filtering, and decision logging.
This project is intended to illustrate defensible inference patterns and support audit discussions, not to serve as a turnkey deployment system.
Interactive decision-support tool for analyzing cost, quality, and latency tradeoffs in AI system design.
Used to support documented decision-making during system planning and governance review.
Benchmarking toolkit for evaluating bias and disparate impact across protected attributes and use cases.
Designed to support pre-deployment bias evaluation and documentation of known limitations.
Interpretability toolkit providing multiple explanation methods (e.g., SHAP, LIME, attention visualization) through a unified interface.
Used to support explainability analysis and audit readiness, not post-hoc justification.
Document integrity evaluation toolkit combining cryptographic hashing and ML-based tamper detection.
Designed to support forensic review and compliance workflows, with traceable decision signals suitable for audit and legal contexts.
Reference implementations for uncertainty estimation and calibration analysis, including conformal prediction, ensembles, and Bayesian methods.
Used to evaluate whether model confidence aligns with observed reliability.
Datasets published to support evaluation, benchmarking, and reproducible research
Bias evaluation dataset for NLP tasks across multiple protected categories.
Dataset for evaluating document integrity and tamper detection methods.
Evaluation dataset for healthcare AI safety analysis with expert physician annotations.
Multi-domain benchmark for uncertainty calibration and confidence assessment.
Join a community focused on accountable AI
Browse repositories for governance, safety, and evaluation tasks labeled "good first issue" or "help wanted."
Implement changes following documented contribution and review standards.
Submit a PR with a clear description of scope and rationale. Reviews are conducted with a focus on correctness, documentation, and reproducibility.
These open-source resources inform and support:
Open source enables scrutiny.
Independent audits establish accountability.
Whether you are a researcher, engineer, or student, you are welcome to contribute to work focused on defensible, auditable AI systems.