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Valutazione umana (Human Evaluation)
Human evaluation is the method by which real people judge the quality of an AI system's outputs, complementing or replacing automated metrics. It is especially useful for aspects that are hard to quantify with a single number, such as the naturalness of a text, the logical coherence of a response, or how useful it feels to the recipient. It is often used when automated benchmarks fail to capture important nuances.
In practice, human raters read or compare different outputs and assign a judgment according to predefined criteria, such as a numeric scale or a pairwise comparison between two responses. To reduce subjectivity, multiple raters assess the same output and inter-rater agreement is calculated. Results are then aggregated into overall scores or preference rankings.
It is widely used to validate language models before release, to train systems through human feedback, and to verify that AI responses are appropriate in sensitive contexts such as health or professional advice. Many evaluation platforms pair it with automated tests for a fuller picture.
It has always accompanied research on AI and natural language, but became central with the spread of conversational models, when the quality perceived by real people took on decisive weight in judging a system.
Grace integrates human evaluation into its certification paths: scenarios completed by users can be reviewed by expert evaluators to refine the automatically calculated scores.
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