Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models
Ned Cooper, Alexandra Zafiroglu
TL;DR
This paper analyzes how feedback features in public facing LLM interfaces, specifically ChatGPT, shape user participation in model iteration through the lens of the mechanisms and conditions affordances framework. Using a mixed approach of pilot and main survey data from 526 respondents, the authors show that feedback is dominated by simple, frequent, performance-related signals (thumbs up/down, regeneration feedback) and that collective discussion and iterative revision of feedback are discouraged. The findings highlight scale induced by standardized interfaces may reproduce power imbalances and marginalize non technical perspectives, urging infrastructuring and deliberative processes to broaden participation. The authors propose redesign directions including bidirectional dialogue, community deliberation, and domain-specific infrastructure to sustain meaningful engagement about LLM purposes and applications.
Abstract
Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces, shifting the dynamics of participation in AI development. This article examines how interactive feedback features in ChatGPT's interface afford user participation in LLM iteration. Drawing on a survey of early ChatGPT users and applying the mechanisms and conditions framework of affordances, we analyse how these features shape user input. Our analysis indicates that these features encourage simple, frequent, and performance-focused feedback while discouraging collective input and discussions among users. Drawing on participatory design literature, we argue such constraints, if replicated across broader user bases, risk reinforcing power imbalances between users, the public, and companies developing LLMs. Our analysis contributes to the growing literature on participatory AI by critically examining the limitations of existing feedback processes and proposing directions for redesign. Rather than focusing solely on aligning model outputs with specific user preferences, we advocate for creating infrastructure that supports sustained dialogue about the purpose and applications of LLMs. This approach requires attention to the ongoing work of "infrastructuring" - creating and sustaining the social, technical, and institutional structures necessary to address matters of concern to stakeholders impacted by LLM development and deployment.