揭示二分类模型中的捷径学习现象，提升语音反欺骗与生物识别的可靠性
Shortcut Learning in Binary Classifier Black Boxes: Applications to Voice Anti-Spoofing and Biometrics
- 通过干预与观察视角分析黑箱模型，识别数据偏差对预测的影响
- 在语音防伪和说话人验证任务中发现模型依赖表面特征而非真实内容
- 为可解释人工智能提供新方法，适合关注模型公平性的研究者
深度学习模型在数据驱动应用中的广泛应用引发了对数据集和模型潜在偏见的关注。被忽视或隐藏的数据与模型偏差可能导致意外结果。本文针对数据集偏差问题，探讨二分类器中的‘捷径学习’（Clever Hans效应）。提出一种新型框架，用于分析黑箱分类器，并评估训练与测试数据对分类器得分的影响。该框架结合干预与观察视角，采用线性混合效应模型进行事后分析。通过超越错误率的评估，揭示有偏数据集对分类器行为的影响。实验在语音反欺骗与说话人验证任务中验证了方法的有效性，涵盖统计模型与深度神经网络。研究结果对其他领域应对偏见及推进可解释人工智能具有广泛意义。
原文摘要 · Abstract (English)
The widespread adoption of deep-learning models in data-driven applications has drawn attention to the potential risks associated with biased datasets and models. Neglected or hidden biases within datasets and models can lead to unexpected results. This study addresses the challenges of dataset bias and explores ``shortcut learning'' or ``Clever Hans effect'' in binary classifiers. We propose a novel framework for analyzing the black-box classifiers and for examining the impact of both training and test data on classifier scores. Our framework incorporates intervention and observational perspectives, employing a linear mixed-effects model for post-hoc analysis. By evaluating classifier performance beyond error rates, we aim to provide insights into biased datasets and offer a comprehensive understanding of their influence on classifier behavior. The effectiveness of our approach is demonstrated through experiments on audio anti-spoofing and speaker verification tasks using both statistical models and deep neural networks. The insights gained from this study have broader implications for tackling biases in other domains and advancing the field of explainable artificial intelligence.
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