{"data":{"full_name":"AmirhosseinHonardoust/Machine-Learning-Warning-Systems","name":"Machine-Learning-Warning-Systems","description":"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.","stars":18.0,"forks":0.0,"language":null,"license":"MIT","archived":0.0,"subcategory":"ml-drift-detection","last_pushed_at":"2025-12-20T19:27:28+00:00","pypi_package":null,"npm_package":null,"downloads_monthly":0.0,"dependency_count":0.0,"commits_30d":null,"reverse_dep_count":0.0,"maintenance_score":6.0,"adoption_score":6.0,"maturity_score":13.0,"community_score":0.0,"quality_score":25.0,"quality_tier":"experimental","risk_flags":"['no_package', 'no_dependents']"},"meta":{"timestamp":"2026-04-12T20:30:16.423195+00:00"}}