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AI Responsabile
Responsible AI is the overall approach to designing, developing, and managing artificial intelligence systems that systematically accounts for safety, fairness, transparency, privacy, and social impact, not just technical performance such as accuracy or speed. It is not a single technique but a set of organizational and engineering practices that accompany a model from conception to production monitoring.
In practice, a responsible AI program typically includes guiding principles published by the organization, ethical review processes before releasing new models or features, documentation such as model cards and impact assessments, reporting mechanisms for users who encounter problems, and internal governance structures involving legal, security, and product teams alongside technical ones.
It has become an operational requirement, not just a values statement, for companies developing or adopting AI at scale: regulations such as the EU AI Act impose concrete risk management obligations, and large enterprise customers increasingly demand responsible AI assurances as a contractual condition for adopting a vendor. Many AI labs publish periodic transparency reports documenting how they apply these principles.
The term gained traction starting in the mid-2010s, when major AI research labs began publishing the first ethical frameworks, and it consolidated as a standard industry category as large generative language models expanded from 2022 onward.
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