Contextual Confidence and Generative AI

TL;DR

Contextual Confidence and Generative AI analyzes how AI-generated content disrupts the ability to identify and protect communication context. It introduces two strategy families—containment and mobilization—to stabilize context, spanning content provenance, community-sourced context, digital identities, watermarking, model verification, relational authentication, and contextual training. The paper emphasizes layering these strategies across AI model development, identity, messaging, and data management to reestablish norms that balance authenticity and privacy, while acknowledging limitations in centralized systems and the need for empirical validation. Overall, it argues that embedding contextual confidence into policy, tooling, and design can curb misuse, reduce context collapse, and guide responsible evolution of AI-mediated communication.

Abstract

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.

Figures (1)

• Figure 1: Summary of parties to pursue strategies discussed in this report.