{"id":11736,"date":"2025-01-21T12:56:41","date_gmt":"2025-01-21T20:56:41","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/?post_type=example&p=11736"},"modified":"2025-02-03T09:37:03","modified_gmt":"2025-02-03T17:37:03","slug":"linguistic-uncertainty-expressions-2a-match-the-level-of-precision-in-ui-communication-with-the-system-performance-language","status":"publish","type":"example","link":"https:\/\/www.microsoft.com\/en-us\/haxtoolkit\/example\/linguistic-uncertainty-expressions-2a-match-the-level-of-precision-in-ui-communication-with-the-system-performance-language\/","title":{"rendered":"Linguistic uncertainty expressions | 2A: Match the level of precision in UI communication with the system performance – Language"},"content":{"rendered":"\n\n\n
\"blue<\/figure>\n\n\n\n

First-person expressions of uncertainty (Kim et al., 2024<\/a>) set expectations for how well the system can do what it can do (Guideline 2<\/a>) by using hedging language (Pattern 2A<\/a>), such as \u201cI am 60% confident that\u2026\u201d, \u201cI am not entirely certain that\u2026\u201d, \u201cI\u2019m not sure, but it seems\u2026\u201d to convey that the AI recommendations might be wrong.<\/p>\n\n\n\n

The techniques used in this example have the potential to foster appropriate reliance on AI, by reducing overreliance. Keep in mind that overreliance mitigations can backfire. Be sure to test such mitigations in context, with your AI system\u2019s actual users. Learn more about\u00a0overreliance on AI<\/a> and\u00a0appropriate reliance on generative AI<\/a>.<\/p>\n\n\n\n

\"An
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