interpretable AI

Decoding the Black Box: Comparing Interpretable ML Methods

A comprehensive study compares leading interpretable ML techniques including SHAP, LIME, and attention mechanisms, providing crucial insights for building transparent AI systems in detection and authenticity applications.

AI safety

Neuroscience-Inspired AI: Actions, Memory and Safety Design

New research proposes integrating actions, compositional structure, and episodic memory from neuroscience to build safer, more interpretable AI systems that could transform how we approach AI trustworthiness.