Exploring the "Banality" of Deception in Generative AI

TL;DR

This paper discusses the subtle and normalized forms of deception in generative AI, especially chatbots, emphasizing the need for user awareness and safeguards to prevent manipulation.

Contribution

It introduces the concept of 'banal deception' in generative AI and suggests strategies like raising awareness and regulatory measures to mitigate it.

Findings

Deception in AI is increasingly embedded in default settings and interactions.

Users' involvement in deception can be leveraged to develop protective interventions.

The paper proposes raising awareness and regulatory improvements as safeguards.

Abstract

Current approaches to addressing deceptive design largely focus on visible interface manipulations, commonly referred to as "dark patterns". With the rise of generative AI, deception is becoming more difficult to spot and easier to live with, as it is quietly embedded in default settings, automated suggestions, and conversational interactions rather than discrete interface elements. These subtle, normalised forms of influence, which Simone Natale frames as "banal deception", shape everyday digital use and blur the line between AI-enabled assistance and manipulation. This position paper explores banality as a lens through which to reason through deception in generative AI experiences, especially with chatbots. We explore what Natale describes as users' own involvement in their deception, and argue that this perspective could lead to future work for introducing friction to safeguard users from deception in generative AI interactions, such as empowering users through raising awareness, providing them with intervention tools, and regulatory or enforcement improvements. We present these concepts as points for discussion for the deceptive design scholarly community.