Exploring the "Banality" of Deception in Generative AI

2026-05-07

AI Summary

This study addresses the phenomenon of “banal deception” in generative AI—subtle, normalized forms of misinformation that blur the boundary between assistance and manipulation, often leading users to unwittingly participate in their own misdirection. Drawing on conceptual frameworks from human-computer interaction, philosophy, and communication studies, the work offers a critical analysis of the covert influence mechanisms embedded in generative chatbots. It proposes strategic interventions—including the intentional introduction of friction, enhanced user awareness, and improved regulatory oversight—to inform the development of user-empowering tools, design of effective intervention mechanisms, and refinement of policy frameworks. By elucidating the dynamics of banal deception, this research provides both theoretical grounding and practical pathways for mitigating deceptive practices in AI systems.

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.

Problem

• banal deception
• generative AI
• deceptive design
• dark patterns
• AI manipulation

Innovation

• banal deception
• generative AI
• dark patterns
• user agency
• design friction