Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...

Introduction: Local interpretability methods such as LIME and SHAP are widely used to explain model decisions. However, they rely on assumptions of local continuity that often fail in recursive, ...

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According to Anthropic (@AnthropicAI), new interpretability methods have been developed that allow researchers to trace the 'thinking' steps of AI models, which could enhance transparency and trust in ...

This talk will attempt to demystify, for a non-technical audience, the current state of neural network explainability and interpretability, as well as trace the boundaries of what is in principle ...

Abstract: Neural networks, which are a type of deep learning model, are massively criticized due to their ‘black box’ approach, which does not allow interpreting their decisions. In this research, ...

Successful machine learning projects require that we negotiate trade-offs between the accuracy of our predictions and the interpretability of our predictions. This is why it is important to carefully ...