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auditability

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A long-form article and practical framework for designing machine learning systems that warn instead of decide. Covers regimes vs decimals, levers over labels, reversible alerts, anti-coercion UI patterns, auditability, and the “Warning Card” template, so ML preserves human agency while staying useful under uncertainty.

  • Updated Dec 20, 2025

This project integrates Hyperledger Fabric with machine learning to enhance transparency and trust in data-driven workflows. It outlines a blockchain-based strategy for data traceability, model auditability, and secure ML deployment across consortium networks.

  • Updated May 29, 2025
  • Shell

Governance beneath the model. Custody before trust. Open for audit. Constitutional Grammar for Multi-Model AI Federations, Firmware Specification • Zero-Touch Alignment • Public Release v1.0

  • Updated Dec 30, 2025
  • Rich Text Format

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