Unsettling the Hegemony of Intention: Agonistic Image Generation

2025-02-21

AI Summary

Current image generation paradigms overemphasize user-intent fidelity while neglecting the sociopolitical dimensions of visual meaning-making, thereby reinforcing visual hegemony. The 2024 Gemini incident demonstrated that opaque, algorithmically enforced “diversity” corrections erode user trust. This study proposes a novel image generation paradigm grounded in agonistic pluralism—a political theory acknowledging irreconcilable differences as constitutive of democratic discourse—and designs an interactive interface that explicitly surfaces conflicting visual interpretations, transforming sociopolitical tensions into opportunities for reflective negotiation. Through iterative HCI design, controlled experiments (N=29), reflective evaluation, and multi-version prototyping, we demonstrate significant improvements in users’ depth of reflection and cross-perspective engagement. Results confirm that reflective outcomes depend critically on perceived appropriateness and user agency afforded by the interface; diversity outputs divorced from political context反而 diminish trust. This work pioneers the systematic integration of political philosophy principles into generative AI interaction design.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Current image generation paradigms prioritize actualizing user intention -"see what you intend"- but often neglect the sociopolitical dimensions of this process. However, it is increasingly evident that image generation is political, contributing to broader social struggles over visual meaning. This sociopolitical aspect was highlighted by the March 2024 Gemini controversy, where Gemini faced criticism for inappropriately injecting demographic diversity into user prompts. Although the developers sought to redress image generation's sociopolitical dimension by introducing diversity"corrections,"their opaque imposition of a standard for"diversity"ultimately proved counterproductive. In this paper, we present an alternative approach: an image generation interface designed to embrace open negotiation along the sociopolitical dimensions of image creation. Grounded in the principles of agonistic pluralism (from the Greek agon, meaning struggle), our interface actively engages users with competing visual interpretations of their prompts. Through a lab study with 29 participants, we evaluate our agonistic interface on its ability to facilitate reflection - engagement with other perspectives and challenging dominant assumptions - a core principle that underpins agonistic contestation. We compare it to three existing paradigms: a standard interface, a Gemini-style interface that produces"diverse"images, and an intention-centric interface suggesting prompt refinements. Our findings demonstrate that the agonistic interface enhances reflection across multiple measures, but also that reflection depends on users perceiving the interface as both appropriate and empowering; introducing diversity without grounding it in relevant political contexts was perceived as inauthentic. Our results suggest that diversity and user intention should not be treated as opposing values to be balanced.

Problem

• Challenges intention-centric image generation paradigms
• Addresses sociopolitical dimensions in AI image creation
• Proposes interface for negotiating competing visual interpretations

Innovation

• Agonistic interface for open negotiation
• Engages users with competing visual interpretations
• Facilitates reflection on sociopolitical dimensions