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.

翻译：AI多元主义通常被表述为一个代表多元价值观、偏好、用户或输出的问题。本文认为这一表述是不完整的，因为AI系统也强加本体论：它们定义了何为实体、关系、特征、危害、利益及有效证据形式。我们将本体论扁平化定义为将情境化、有争议且具有历史特定性的意义转化为被视作中立且难以质疑的技术范畴、代理指标、聚合规则或基准目标。本文通过有限的概念与定性综合研究，涵盖价值多元主义、多元对齐、参与式与民主式AI、程序正义、科技与社会研究、问责研究、11场专家访谈的聚合主题及三个城市AI伴随案例。这些案例表明，多元主义方法能够改进或结构化模型行为，但同时仍在受影响主体获得程序性地位之前压缩了类别、代理指标、聚合规则与修订权利。我们提出多元主义生命周期治理（PLG）作为初步的定性审计框架，用于记录本体论开放性、知识包容性、程序权威性、评估多元性与生命周期问责制。PLG并非被呈现为经过验证的评分工具，而是一个用于揭示多元AI的证据与治理条件的框架。