As AI becomes increasingly embedded in daily life, it has been shown to fail critically, cause harm, and spark public controversy, prompting affected communities, workers, and public-interest groups to contest it. Yet how these contestations unfold in practice remains underexplored. We address this gap by developing an empirically grounded account of AI contestation dynamics. We do so through a thematic analysis of 43 real-world cases in which affected actors direct demands toward those responsible for AI development and deployment, seeking redress, influence, or changes to AI practices. Situating our work within Bovens's relational model of accountability, we conceptualize contestation as accountability-seeking: a dynamic, iterative process in which actors "from below" direct explicit demands at actors "from above," who respond by accepting, resisting, or circumventing accountability. Our analysis produces empirically grounded categories of contestation strategies, institutional response tactics, outcome types, and the contextual factors that shape them, illuminating how accountability is pursued and evaded in practice. We show that those being contested often deploy a range of strategies to limit their accountability. Based on these insights, we offer guidance for researchers, policymakers, advocates, and other stakeholders seeking to support effective AI contestation, with particular attention to anticipating and countering institutional strategies used to evade accountability.

翻译：随着人工智能日益嵌入日常生活，其已被证明存在严重缺陷、造成危害并引发公众争议，促使受影响社区、劳动者和公益组织对其发起质疑。然而，这些质疑在实践中如何展开仍未被充分探究。我们通过构建基于实证的AI质疑动力学理论来弥合这一研究空白。通过对43个真实案例的专题分析（其中受影响行动者直接向AI开发与部署责任方提出诉求，寻求补救、影响力或改变AI实践），我们将研究置于Bovens的关系问责模型中，将质疑概念化为问责诉求：一种动态迭代过程，其中"下层"行动者向"上层"行动者提出明确诉求，后者通过接受、抵制或规避问责来回应。我们的分析产生了基于实证的质疑策略、制度回应策略、结果类型及其影响因素分类，揭示了问责在实践中如何被追求与规避。研究表明，被质疑方常运用多种策略限制自身责任。基于这些发现，我们为研究人员、政策制定者、倡导者及其他希望支持有效AI质疑的利益相关方提供指导，特别关注如何预判并反制逃避问责的制度化策略。