The Multiple Dimensions of Spuriousness in Machine Learning

2024-11-07

arXiv.org

AI Summary

Machine learning models frequently exploit spurious correlations—statistical associations that lack causal validity—thereby compromising performance, fairness, and robustness. To address this, we move beyond conventional statistical or purely causal dichotomies and propose the first four-dimensional criterion for spuriousness: task relevance, generalizability, human plausibility, and harmlessness. We conceptualize spuriousness as a context-dependent, value-laden, and socially negotiated process. Through a large-scale literature review and interdisciplinary conceptual analysis—integrating causal inference, fairness theory, cognitive modeling, and AI ethics—we systematically deconstruct the multifaceted nature of spurious correlations and their concrete implications for model design, evaluation, and deployment. Our framework provides a theoretically grounded yet practically actionable foundation for trustworthy AI, advancing spuriousness research from descriptive detection toward value-sensitive, normative construction.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Ethics, Bias, and Fairness
• Reasoning under Uncertainty: Causality

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Examining how spurious correlations impact ML model performance and fairness
• Identifying pragmatic frames to assess correlation desirability in ML research
• Analyzing technical and ethical considerations in defining spurious correlations

Innovation

• Examines spurious correlations via pragmatic frames
• Identifies four frames: relevance, generalizability, human-likeness, harmfulness
• Links correlation desirability to technical, epistemic, ethical factors