To Trust Or Not To Trust A Classifier (1805.11783v2)

Abstract: Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI. While the bulk of the effort in machine learning research has been towards improving classifier performance, understanding when a classifier's predictions should and should not be trusted has received far less attention. The standard approach is to use the classifier's discriminant or confidence score; however, we show there exists an alternative that is more effective in many situations. We propose a new score, called the trust score, which measures the agreement between the classifier and a modified nearest-neighbor classifier on the testing example. We show empirically that high (low) trust scores produce surprisingly high precision at identifying correctly (incorrectly) classified examples, consistently outperforming the classifier's confidence score as well as many other baselines. Further, under some mild distributional assumptions, we show that if the trust score for an example is high (low), the classifier will likely agree (disagree) with the Bayes-optimal classifier. Our guarantees consist of non-asymptotic rates of statistical consistency under various nonparametric settings and build on recent developments in topological data analysis.

• The paper presents the trust score, a metric that contrasts nearest-neighbor distances to quantify the reliability of classifier predictions.
• It employs density-based clustering and topological data analysis to filter outliers and achieve robust estimation under diverse data distributions.
• Empirical results show that trust scores outperform conventional confidence metrics in low to medium-dimensional settings, while highlighting challenges in high-dimensional cases.

Overview of "To Trust Or Not To Trust A Classifier"

In the context of ML, high performance is not solely about accuracy. Equally crucial is the ability to determine when a classifier's predictions are reliable—especially in applications such as medical diagnosis or autonomous driving where erroneous decisions carry significant consequences. The paper "To Trust Or Not To Trust A Classifier" by Heinrich Jiang et al. proposes a novel approach to gauge trust in classifier predictions using what they term as the "trust score."

Trust Score: Concept and Methodology

The conventional method to assess the reliability of classifier predictions is to use its confidence scores, often derived from the model's output (such as the softmax probabilities in neural networks). However, these scores often suffer from calibration issues and may not rank predictions reliably. Jiang et al. introduce the trust score as an alternative measure, which evaluates trust by relating the classifier's prediction to a modified nearest-neighbor model.

The trust score is calculated by the ratio of distances: it measures the proximity of a testing sample to the nearest high-density region of a different class versus that of the predicted class. This distance is computed with respect to an $\alpha$-high-density-set of training data points, which filters outliers based on data density—a process grounded in topological data analysis and density-based clustering.

Theoretical Foundations and Guarantees

The authors provide rigorous theoretical backing for the trust score. They argue that under mild distributional assumptions, a high trust score signifies likely agreement with Bayes-optimal decisions, whereas a low trust score points to potential disagreement. This theoretical analysis offers non-asymptotic rates of statistical consistency across various data distributions, including those lying on manifolds, thus making the results applicable to high-dimensional space data with lower intrinsic dimensionality.

Empirical Validation and Applicability

Extensive experiments demonstrate the trust score's superior ability to distinguish correct from incorrect predictions across diverse datasets and classifier types, namely, neural networks, random forests, and logistic regression models. The trust score consistently surpasses baseline approaches like the classifier's inherent confidence scores and a simpler nearest-neighbor ratio, especially in low to medium-dimensional data settings.

However, on high-dimensional datasets like CIFAR-10 and CIFAR-100, the performance gain is less pronounced. This suggests that while the trust score adds significant value in mid to low-dimensional contexts, its utility in high-dimensional settings might require additional research, possibly incorporating refined feature extraction or dimensionality reduction techniques.

Future Directions

This research advances our understanding of classifier reliability and suggests that exploring improved trust metrics could significantly enhance the robustness of ML systems. Future work might delve into alternative distance metrics, integration with ensemble methods, or application-specific calibration of trust scores. The open-source release of the trust score implementation facilitates further exploration and adaptation to other domains, potentially contributing to safer and more interpretable AI models.

In conclusion, trust scores offer a promising avenue to enhance decision-making frameworks, particularly in mission-critical applications. This work marks a substantive step toward ensuring that ML systems not only perform well but also provide users with the means to understand the validity and reliability of their predictions.