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

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 …

The Trust Nexus: A Framework for Building Scalable, Transparent, and Unbiased AI Systems

Part I: The Crisis of Trust: Understanding AI Bias and Its Consequences The rapid integration of artificial intelligence into core business and societal functions has created unprecedented opportunities for efficiency Read More …

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

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 …