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Explanation

Features​

  1. Encourages Accountability

    • Incorporate essential model information (metadata, dataset details, fairness, explainability) at training time, ensuring AI models remain transparent from development to deployment.

  2. Semi-Automated Capture

    • Automated Fairness and Explainability scanners compute demographic parity, equal odds, SHAP-based feature importances, etc., for easy integration into Model Cards.

  3. Machine-Actionable Model Cards

    • Produce a structured JSON representation for ingestion into the Patra Knowledge Base. Ideal for advanced queries on model selection, provenance, versioning, or auditing.

  4. Flexible Repository Support

    • Pluggable backends for storing models/artifacts on Hugging Face or GitHub, unifying the model publishing workflow.

  5. Versioning & Model Relationship Tracking

    • Maintain multiple versions of a model with recognized edges (e.g., revisionOf, alternateOf) using embedding-based similarity. This ensures clear lineages and easy forward/backward provenance.