The roads toward explainability

Interpretability and explainability in machine learning are crucial yet often underspecified concepts that help demystify black-box models by providing insights into what models have learned. While intrinsic interpretability refers to models that are naturally understandable, such as linear regression, post-hoc methods like SHAP, LIME, and Anchors are used to explain complex models post-training by generating surrogate models. Techniques like K-nearest neighbors and influential instances use similar examples to elucidate model predictions, while counterfactual and adversarial analyses employ dissimilar examples to offer contrastive explanations, enhancing robustness and revealing potential failure modes. Error analysis utilizes these explainability techniques to identify model weaknesses, providing a scientific approach to improve performance. For instance, LIME scores in natural language processing tasks can highlight which features drive incorrect predictions, leading to training data augmentation for better accuracy. Evaluating adversarial examples can also expose significant predictive features, indicating potential over-reliance on specific data aspects. The article emphasizes integrating various explainability dimensions in error analysis to derive actionable insights, advocating for a deeper understanding of machine learning models through tools like Openlayer.