AI Pluralism and the Worlds It Misses

2026-06-14

AI Summary

Current AI systems often reduce complex, context-dependent, and contested realities to ostensibly neutral technical categories due to implicit ontological assumptions, resulting in what this paper terms “ontological flattening.” To address this issue, the study integrates interdisciplinary perspectives from value pluralism, participatory AI, and procedural justice, drawing on expert interviews and urban AI case studies. It proposes a Pluralistic Lifecycle Governance (PLG) framework that emphasizes epistemic inclusivity, procedural legitimacy, and pluralistic evaluation throughout the AI lifecycle. The framework introduces a non-scoring governance audit scaffold designed to render AI systems’ ontological assumptions and governance conditions explicit and negotiable, thereby offering a structured analytical tool to support the coexistence of multiple values.

Abstract

AI pluralism is often framed as a problem of representing diverse values, preferences, users, or outputs. This paper argues that this framing is incomplete because AI systems also impose ontologies: they define what counts as an entity, relation, feature, harm, benefit, and valid form of evidence. We define ontological flattening as the conversion of situated, contested, and historically specific meanings into a restricted technical category, proxy, aggregation rule, or benchmark target that is treated as neutral and difficult to contest. The paper develops a bounded conceptual and qualitative synthesis across value pluralism, pluralistic alignment, participatory and democratic AI, procedural justice, science and technology studies, accountability research, aggregate themes from 11 expert interviews, and three urban AI companion cases. The cases illustrate how pluralistic methods can improve or structure model behavior while still compressing categories, proxies, aggregation rules, and revision rights before affected actors have procedural standing. We introduce Pluralistic Lifecycle Governance (PLG) as a preliminary qualitative audit scaffold for documenting ontological openness, epistemic inclusion, procedural authority, evaluation pluralism, and lifecycle accountability. PLG is not presented as a validated scoring instrument; it is a framework for making the evidence and governance conditions of pluralistic AI explicit.

Problem

• AI pluralism
• ontological flattening
• procedural justice
• epistemic inclusion
• lifecycle accountability

Innovation

• ontological flattening
• pluralistic lifecycle governance
• AI pluralism
• procedural justice
• epistemic inclusion