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New framework proposes 'Justified True Output' for AI trust

By PulseAugur Editorial · [1 sources] ·

A new paper proposes a three-step framework for establishing rational trust in generative AI outputs, addressing the challenges posed by the "generality problem" and the "Gettier machine" phenomenon. The proposed "Justified True Output" (JTO) criterion suggests that users should acquire, understand, and independently verify AI-generated information. This approach aims to provide a stakes-indexed method for determining when AI outputs can be rationally trusted, even as models improve and the concept of the extended mind is considered. AI

IMPACT Offers a new epistemological framework for evaluating the reliability of generative AI outputs, potentially influencing how users interact with and trust AI systems.

RANK_REASON Academic paper proposing a new framework for AI trust. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework proposes 'Justified True Output' for AI trust

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Academic paper proposing a new framework for AI trust. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

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

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

    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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