decision-systems

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AmirhosseinHonardoust / Machine-Learning-Warning-Systems

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.

product-design machine-learning risk ux uncertainty calibration governance warnings fairness accountability human-in-the-loop interpretability explainable-ai ai-ethics mlops systems-thinking responsible-ai counterfactuals auditability decision-systems

Updated Dec 20, 2025

AmirhosseinHonardoust / The-Twin-Test-High-Stakes-ML

Star 9

A long-form article introducing the Twin Test: a practical standard for high-stakes machine learning where models must show nearest "twin" examples, neighborhood tightness, mixed-vs-homogeneous evidence, and "no reliable twins" abstention. Argues similarity and evidence packets beat probability scores for trust and safety.

product-design machine-learning risk uncertainty calibration nearest-neighbors knn governance human-in-the-loop interpretability similarity-search case-based-reasoning explainable-ai ai-ethics mlops out-of-distribution responsible-ai data-drift high-stakes decision-systems

Updated Dec 26, 2025

mj3b / applied-ai-research-translator

Star 1

A governed system for translating applied AI research into auditable, decision-ready artifacts.

human-in-the-loop ml-production applied-ai ai-governance auditability decision-systems

Updated Dec 27, 2025
Python

opsec-ee / rtka-u

Star 1

"Advanced ternary logic framework for processing uncertainty in computational decision systems—solving the fundamental limitation of binary logic in real-world applications."