The Value of Disagreement in AI Design, Evaluation, and Alignment

TL;DR

This paper highlights the importance of preserving disagreement and diverse perspectives in AI development to prevent homogenization, proposing a normative framework for ethical and epistemic benefits throughout the AI lifecycle.

Contribution

It introduces the concept of perspectival homogenization and offers a normative framework to incorporate disagreement in AI design, evaluation, and governance.

Findings

Perspectival homogenization is an ethical-epistemic risk in AI development.

Disagreement can be epistemically beneficial when properly managed.

The framework guides interventions across AI development stages to preserve diversity.

Abstract

Disagreements are widespread across the design, evaluation, and alignment pipelines of artificial intelligence (AI) systems. Yet, standard practices in AI development often obscure or eliminate disagreement, resulting in an engineered homogenization that can be epistemically and ethically harmful, particularly for marginalized groups. In this paper, we characterize this risk, and develop a normative framework to guide practical reasoning about disagreement in the AI lifecycle. Our contributions are two-fold. First, we introduce the notion of perspectival homogenization, characterizing it as a coupled ethical-epistemic risk that arises when an aspect of an AI system's development unjustifiably suppresses disagreement and diversity of perspectives. We argue that perspectival homogenization is best understood as a procedural risk, which calls for targeted interventions throughout the AI development pipeline. Second, we propose a normative framework to guide such interventions, grounded in lines of research that explain why disagreement can be epistemically beneficial, and how its benefits can be realized in practice. We apply this framework to key design questions across three stages of AI development tasks: when disagreement is epistemically valuable; whose perspectives should be included and preserved; how to structure tasks and navigate trade-offs; and how disagreement should be documented and communicated. In doing so, we challenge common assumptions in AI practice, offer a principled foundation for emerging participatory and pluralistic approaches, and identify actionable pathways for future work in AI design and governance.