Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence (AI) research. While such an approach enables the automatic discovery of patterned relationships within big data corpora, it is susceptible to failure modes when unintended correlations are captured. This vulnerability has expanded interest in interrogating spuriousness, often critiqued as an impediment to model performance, fairness, and robustness. In this article, we trace deviations from the conventional definition of statistical spuriousness-which denotes a non-causal observation arising from either coincidence or confounding variables-to articulate how ML researchers make sense of spuriousness in practice. Drawing on a broad survey of ML literature, we conceptualize the "multiple dimensions of spuriousness," encompassing: relevance ("Models should only use correlations that are relevant to the task."), generalizability ("Models should only use correlations that generalize to unseen data"), human-likeness ("Models should only use correlations that a human would use to perform the same task"), and harmfulness ("Models should only use correlations that are not harmful"). These dimensions demonstrate that ML spuriousness goes beyond the causal/non-causal dichotomy and that the disparate interpretative paths researchers choose could meaningfully influence the trajectory of ML development. By underscoring how a fundamental problem in ML is contingently negotiated in research contexts, we contribute to ongoing debates about responsible practices in AI development.

翻译：从数据中学习相关性构成了当今机器学习（ML）与人工智能（AI）研究的基础。尽管这种方法能够自动发现大数据语料库中的模式化关系，但当模型捕捉到非预期的相关性时，便容易陷入失效模式。这一脆弱性促使学界日益关注对虚假相关性的探究，它常被批评为阻碍模型性能、公平性与鲁棒性的因素。本文追溯了与统计虚假相关性传统定义——即指由巧合或混杂变量导致的非因果性观察——的偏离，以阐明ML研究者如何在实践中理解虚假相关性。基于对ML文献的广泛调研，我们提出了“虚假相关性的多重维度”概念框架，其包含：相关性（“模型应仅使用与任务相关的相关性”）、泛化性（“模型应仅使用能泛化至未见数据的相关性”）、类人性（“模型应仅使用人类执行相同任务时会采用的相关性”）以及危害性（“模型应仅使用无危害的相关性”）。这些维度表明，ML中的虚假相关性超越了因果/非因果的二元划分，且研究者选择的不同解释路径可能实质性地影响ML发展的轨迹。通过强调这一ML基础问题如何在研究语境中被具体协商，我们为当前关于AI发展中负责任实践的讨论提供了新的视角。