Explainability through uncertainty: Trustworthy decision-making with neural networks (2403.10168v1)

Abstract: Uncertainty is a key feature of any machine learning model and is particularly important in neural networks, which tend to be overconfident. This overconfidence is worrying under distribution shifts, where the model performance silently degrades as the data distribution diverges from the training data distribution. Uncertainty estimation offers a solution to overconfident models, communicating when the output should (not) be trusted. Although methods for uncertainty estimation have been developed, they have not been explicitly linked to the field of explainable artificial intelligence (XAI). Furthermore, literature in operations research ignores the actionability component of uncertainty estimation and does not consider distribution shifts. This work proposes a general uncertainty framework, with contributions being threefold: (i) uncertainty estimation in ML models is positioned as an XAI technique, giving local and model-specific explanations; (ii) classification with rejection is used to reduce misclassifications by bringing a human expert in the loop for uncertain observations; (iii) the framework is applied to a case study on neural networks in educational data mining subject to distribution shifts. Uncertainty as XAI improves the model's trustworthiness in downstream decision-making tasks, giving rise to more actionable and robust machine learning systems in operations research.

• The paper positions uncertainty estimation as a vital XAI technique, providing local explanations that reveal model reliability.
• It introduces classification with rejection, integrating human oversight to reduce risks from overconfident predictions under distribution shifts.
• It demonstrates improved accuracy in educational data mining by applying Monte Carlo Dropout and Deep Ensembles to guide decision-making.

Explainability through Uncertainty: Trustworthy Decision-Making with Neural Networks

The paper "Explainability through Uncertainty: Trustworthy Decision-Making with Neural Networks" by Arthur Thuy and Dries F. Benoit addresses a critical issue in machine learning: the overconfidence of neural networks (NNs), particularly under data distribution shifts. The authors propose an innovative framework that combines uncertainty estimation and explainable artificial intelligence (XAI) to enhance the trustworthiness and actionability of ML systems, particularly in operations research (OR).

Key Contributions

1. Positioning Uncertainty as XAI: The paper argues that uncertainty estimation should be regarded as an XAI technique, providing local and model-specific explanations. This novel perspective integrates uncertainty estimates into the broader XAI framework, enabling deeper insights into model predictions.
2. Classification with Rejection: The framework introduces a mechanism to reduce misclassification by incorporating human expertise into classification decisions. By using uncertainty estimates, predictions with high uncertainty can be flagged for human intervention, effectively mitigating risks associated with incorrect predictions.
3. Application to Educational Data Mining: To illustrate the framework's utility, the authors apply it to a case paper involving student performance prediction using NNs. This practical example demonstrates how the framework can handle real-world distribution shifts in educational data, thereby improving decision support systems in an educational context.

Methodology

The paper proposes a general uncertainty framework, which includes two stages: uncertainty estimation as XAI and classification with rejection. The uncertainty estimation is conducted using state-of-the-art techniques such as Monte Carlo Dropout and Deep Ensembles, which decompose total uncertainty into data and model uncertainty. The classification with rejection system evaluates predictions based on total uncertainty, employing metrics like non-rejected accuracy, classification quality, and rejection quality to guide human decision-making.

Numerical Results and Implications

The case paper reveals that the standard NN model becomes overconfident under distribution shifts, leading to poor predictions. In contrast, models equipped with uncertainty estimation (MC Dropout and Deep Ensembles) exhibit increased total uncertainty, signaling when predictions should not be trusted. This behavior aligns with the desired functionality of "knowing what it does not know", which is critical for robust decision-making.

• Accuracy Improvements: With small distribution shifts, MC Dropout and Deep Ensembles considerably outperformed standard NNs. Even with large shifts, these methods provided valuable uncertainty estimates that could guide rejection policies and improve the model's accuracy through selective observation rejection.

Theoretical and Practical Implications

• Trust and Robustness: By framing uncertainty as an element of XAI, the research enhances trust in ML systems. The increased sensitivity of uncertainty estimates to distribution changes ensures that users are informed about the reliability of model predictions.
• Actionability: Introducing classification with rejection elevates the practical applicability of ML systems, emphasizing decision accuracy over mere prediction performance.
• Future Directions: The framework opens avenues for integrating uncertainty estimation into other ML models and exploring its role in active learning scenarios.

In conclusion, the paper provides a substantial contribution by advocating for uncertainty as a component of XAI, offering a structured approach for integrating human expertise into ML tasks, and demonstrating its practical benefits in educational data mining. The framework not only augments decision-making reliability but also positions uncertainty as a cornerstone in developing robust and actionable ML systems.