AI summaryⓘ
The authors explain that making AI systems represent many viewpoints isn't just about including different values or users; it also involves deciding what things exist and matter in the system, which they call 'ontologies.' They introduce 'ontological flattening' to describe how complex, real-world meanings get simplified into fixed categories or rules that seem neutral but limit debate. By studying expert opinions and urban AI examples, the authors show that methods aiming for pluralism can still restrict which perspectives have a say. They propose a tool called Pluralistic Lifecycle Governance (PLG) to help track how open and fair AI decision-making is, but note it’s a starting framework, not a finalized scoring system.
AI pluralismontologyontological flatteningvalue pluralismprocedural justiceparticipatory AIaccountabilitygovernance frameworkepistemic inclusionlifecycle evaluation
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.