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Contextual Confidence and Generative AI

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arxiv 2311.01193 v2 pith:EDBQNT2R submitted 2023-11-02 cs.AI

classification cs.AI
keywords communicationcontextgenerativestrategiesabilitychallengesconfidencecontextual
verification ladder T0 review T1 audit T2 compute T3 formal
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Generative AI models perturb the foundations of effective human communication. They present new challenges to contextual confidence, disrupting participants' ability to identify the authentic context of communication and their ability to protect communication from reuse and recombination outside its intended context. In this paper, we describe strategies--tools, technologies and policies--that aim to stabilize communication in the face of these challenges. The strategies we discuss fall into two broad categories. Containment strategies aim to reassert context in environments where it is currently threatened--a reaction to the context-free expectations and norms established by the internet. Mobilization strategies, by contrast, view the rise of generative AI as an opportunity to proactively set new and higher expectations around privacy and authenticity in mediated communication.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Trustworthiness in Stochastic Systems: Towards Opening the Black Box

    cs.CY 2025-01 conditional novelty 6.0 of 10

    The authors define trust-relevant stochasticity as variability at or above a user's valued level of description, and propose latent value modeling to assess user-system value alignment.

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