Definition
The field focused on ensuring AI systems behave as intended, don't cause harm, and remain aligned with human values and goals.
In Depth
AI safety covers alignment (ensuring AI goals match human goals), reliability under stress (handling unexpected inputs safely), interpretability (understanding why models make decisions), and governance (policies and oversight). It's increasingly critical as AI systems become more capable and autonomous.
Example Usage
Implementing content filters and output validation in an AI-powered customer service bot to prevent harmful, biased, or inappropriate responses.
Business Context
AI safety is also a business risk, beyond being an ethical concern. An AI system that generates harmful content or makes biased decisions creates legal liability and brand damage.
Related Terms
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When AI models generate false or nonsensical information that appears plausible.
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AI systems that process and generate multiple data types (text, images, audio, video) in a unified m...
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