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English(EN) From the Gettier Machine to Justified True Output: A Three-Step Account of Rational Trust in Generative AI

新框架提出“证真输出”以建立AI信任

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新论文提出了一个三步框架，用于在生成式AI输出中建立理性信任，解决了“泛化问题”和“盖梯尔机器”现象带来的挑战。提出的“证真输出”（Justified True Output, JTO）标准建议用户应获取、理解并独立验证AI生成的信息。这种方法旨在提供一种与风险挂钩的方法，以确定何时可以理性地信任AI输出，即使模型不断改进并考虑了扩展心智的概念。 AI

影响 提供了一个新的认识论框架来评估生成式AI输出的可靠性，可能影响用户与AI系统的互动方式和信任程度。

排序理由 学术论文，提出AI信任新框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架提出“证真输出”以建立AI信任

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学术论文，提出AI信任新框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tevfik Uyar ·

    从盖梯尔机到“有根据的真实输出”：生成式AI中理性信任的三步论述

    arXiv:2610.38112v1 Announce Type: cross Abstract: As generative artificial intelligence (AI) settles into everyday practices of inquiry, an old epistemological question returns in a new guise: when does a proposition a user takes from an AI output become something the user knows,…

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