A helpful answer is not necessarily a reliable answer. AI systems can generate useful text, recognize patterns and support decisions, yet they can also produce mistakes, expose private information or behave differently across situations.
The U.S. National Institute of Standards and Technology created a voluntary AI Risk Management Framework to help organizations identify and manage such risks. Its approach emphasizes properties including validity, reliability, safety, security, resilience, accountability, transparency and fairness.
In practice, trustworthy AI involves more than evaluating a model once. Developers need to understand the setting in which a system is used, measure its performance, document limitations and monitor changes. The level of caution should rise with the consequences of a mistake.
An AI tool that recommends a movie and a system used in a consequential decision should not be treated as if they carry the same risk. Responsible use starts with understanding that difference.
This article is a source-linked explainer, not a claim of firsthand interviews or exclusive reporting.
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