Tag: Model Interpretability

A Technical Analysis of Post-Hoc Explainability: LIME, SHAP, and Counterfactual Methods

Posted on by uplatzblog

Part 1: The Foundational Imperative for Explainability 1.1 Deconstructing the “Black Box”: The Nexus of Trust, Auditing, and Regulatory Compliance The proliferation of high-performance, complex machine learning models in high-stakes Read More …

Decompiling the Mind of the Machine: A Comprehensive Analysis of Mechanistic Interpretability in Neural Networks

Posted on by uplatzblog

Part I: The Reverse Engineering Paradigm As artificial intelligence systems, particularly deep neural networks, achieve superhuman performance and become integrated into high-stakes domains, the imperative to understand their internal decision-making Read More …