Contextual Confidence and Generative AI
This addresses challenges in identifying authentic contexts and protecting communication from misuse, relevant for users and policymakers in digital communication.
The paper tackles the problem of generative AI disrupting contextual confidence in human communication, and proposes strategies to stabilize communication through containment and mobilization approaches.
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.