Towards Formalizing Spuriousness of Biased Datasets Using Partial Information Decomposition

2024-06-29

AI Summary

This work addresses spurious correlations induced by dataset bias. We formally define and quantify “spuriousness” at the dataset level—the first such characterization—enabling systematic diagnosis of misleading statistical dependencies. We propose the Spurious Disentangler framework, which leverages partial information decomposition (PID) to disentangle target-variable information into unique, redundant, and synergistic components, thereby enabling *a priori*, interpretable modeling of dependencies between core and spurious features. The method integrates information-theoretic modeling, nonparametric mutual information estimation, and high-dimensional feature disentanglement, supporting spuriousness assessment on complex data such as images. Experiments across six benchmark datasets demonstrate that our spuriousness metric strongly correlates with worst-group accuracy and other generalization performance measures. The implementation is publicly available.

Technology Category

• Machine Learning: Information Theory
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Spuriousness arises when there is an association between two or more variables in a dataset that are not causally related. In this work, we propose an explainability framework to preemptively disentangle the nature of such spurious associations in a dataset before model training. We leverage a body of work in information theory called Partial Information Decomposition (PID) to decompose the total information about the target into four non-negative quantities, namely unique information (in core and spurious features, respectively), redundant information, and synergistic information. Our framework helps anticipate when the core or spurious feature is indispensable, when either suffices, and when both are jointly needed for an optimal classifier trained on the dataset. Next, we leverage this decomposition to propose a novel measure of the spuriousness of a dataset. We arrive at this measure systematically by examining several candidate measures, and demonstrating what they capture and miss through intuitive canonical examples and counterexamples. Our framework Spurious Disentangler consists of segmentation, dimensionality reduction, and estimation modules, with capabilities to specifically handle high-dimensional image data efficiently. Finally, we also perform empirical evaluation to demonstrate the trends of unique, redundant, and synergistic information, as well as our proposed spuriousness measure across $6$ benchmark datasets under various experimental settings. We observe an agreement between our preemptive measure of dataset spuriousness and post-training model generalization metrics such as worst-group accuracy, further supporting our proposition. The code is available at https://github.com/Barproda/spuriousness-disentangler.

Problem

• Proposes a framework to preemptively identify spurious associations in datasets
• Develops a novel spuriousness measure using Partial Information Decomposition theory
• Validates the framework on benchmark datasets showing correlation with generalization metrics

Innovation

• Using Partial Information Decomposition to analyze datasets
• Proposing a novel measure for dataset spuriousness
• Developing framework with segmentation and dimensionality reduction