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.
This project offers a practical guide for designing machine learning systems that alert users to potential issues without making decisions for them. It helps professionals, such as HR managers, risk analysts, or operations engineers, integrate ML insights into their workflows in a way that preserves human judgment. The framework takes in raw data and model outputs, and provides a structured approach for creating ethical warning interfaces and policies.
Use this if you are building or overseeing an ML system and want to ensure it empowers human decision-makers rather than implicitly controlling them.
Not ideal if your ML system's core function is to automate decisions entirely without human intervention or if you primarily need technical model optimization rather than ethical design guidance.
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MIT
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Last pushed
Dec 20, 2025
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