Selective Classification: Unveiling the Potential Amplification of Inequalities among Groups
Introduction:
Selective classification is a valuable approach in situations where model errors can have severe consequences, such as in the medical field. It allows models to abstain from making predictions when they are uncertain, ultimately improving accuracy. However, our recent research has found that selective classification may not always improve accuracy for certain subpopulations of data. For instance, in diagnosing pleural effusion from chest X-rays, selective classification did not significantly enhance accuracy for patients with pleural effusion who have not yet received treatment. This highlights the need for caution when using selective classification in real-world applications. In this article, we provide an overview of selective classification, present empirical evidence of its limitations, discuss theoretical results, and propose methods for building more equitable selective classifiers.
Full Article: Selective Classification: Unveiling the Potential Amplification of Inequalities among Groups
Selective Classification: A Useful but Flawed Approach for Model Deployment
Introduction
Selective classification, a method that allows models to abstain from making predictions when uncertain, has gained popularity in various fields like medicine, vision, and NLP. By only making predictions when confident, selective classifiers can improve accuracy significantly. However, a recent study published in ICLR has revealed that while selective classification often enhances overall accuracy, it can fail to improve accuracy and even hurt it for specific subpopulations of data. This finding highlights the need for caution when using selective classification in real-world applications.
The Failure of Selective Classification in Pleural Effusion Diagnosis
To illustrate the critical failure mode of selective classification, let’s consider the task of diagnosing pleural effusion from chest X-rays. In this scenario, selective classification improves average accuracy but fails to improve accuracy for the most relevant subgroup of patients – those with pleural effusion who haven’t received treatment yet. This finding suggests that selective classification might not be the ideal tool for resolving accuracy differences between subgroups in medical diagnoses.
Understanding Selective Classification Basics
Selective classification differs from standard classification by allowing models to abstain from making predictions when they lack confidence. By abstaining on examples they are likely to classify incorrectly, selective classifiers aim to increase average accuracy. The decision to abstain is based on a confidence threshold, and examples with confidence below the threshold are not predicted. Researchers typically measure selective classifiers’ performance using accuracy on predicted examples and coverage (the fraction of examples predicted).
The Magnification of Accuracy Disparities with Selective Classification
While previous studies have focused on the average accuracy of selective classifiers, the recent research highlights the importance of accuracy disparities between subgroups. In datasets where models latch onto spurious correlations, selective classification can lead to magnified accuracy disparities. For example, in the pleural effusion task, the model might learn to predict the presence of a chest drain instead of directly diagnosing pleural effusion, resulting in high accuracy for some subgroups and low accuracy for others. Selective classification does not necessarily resolve these accuracy discrepancies.
The Limitations of Selective Classification
Theoretical analysis reveals that selective classification rarely improves accuracy as the confidence threshold decreases and does not effectively reduce full-coverage accuracy disparities. The results from various tasks, including hair color classification, bird type classification, pleural effusion classification, toxicity classification, and natural language inference, demonstrate that worst-group accuracies do not consistently increase and can even decrease in some cases. This implies that practitioners should be cautious when relying solely on selective classification to improve accuracy for different subgroups.
Conclusion
While selective classification has its merits, such as improving average accuracy and allowing models to abstain when uncertain, it is not a foolproof approach. The recent research highlights the potential failure modes of selective classification, particularly in terms of accuracy disparities between subgroups. Future efforts should focus on developing more equitable selective classifiers and exploring alternative methods to address accuracy differences in diverse populations.
Summary: Selective Classification: Unveiling the Potential Amplification of Inequalities among Groups
Selective classification is an effective approach for deploying models in settings where errors can have severe consequences, such as in medicine. By allowing models to abstain when uncertain, selective classification can improve accuracy. However, our recent research shows that it may not always improve accuracy for certain subgroups of the data. We use the example of diagnosing pleural effusion from chest X-rays to demonstrate this issue. While selective classification improves average accuracy, it does not significantly improve accuracy for those with pleural effusion who have not yet been treated. We caution ML practitioners about potential failure modes and suggest methods for building more equitable selective classifiers.
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