Xiaoming Liu Highlights the Role of Explainable AI in Building Trustworthy and Privacy-Preserving Systems

A practical review of explainable AI examines how transparency and interpretability improve trust in high-stakes applications. By introducing ...
InfoQ

Understanding and Debugging Deep Learning Models: Exploring AI Interpretability Methods

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
Forbes

Mechanistic Interpretability: How We Understand AI

One of the major challenges facing businesses using AI is understanding exactly how these models make decisions. Traditionally, AI has been treated like a black box: Inputs go in, outputs come out, ...
Max Planck Society

Demystifying AI Interpretability

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 ...
mccormick.northwestern.edu

Exploring the Connections Among Machine Learning, Interpretability, and Logic

The field of interpretability investigates what machine learning (ML) models are learning from training datasets, the causes and effects of changes within a model, and the justifications behind its ...
InfoQ

DeepMind Open-Sources AI Interpretability Research Tool Tracr

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Dany Lepage discusses the architectural ...
MIT Technology Review

This startup’s new mechanistic interpretability tool lets you debug LLMs

Goodfire wants to make training AI models more like good old-fashioned software engineering. The San Francisco–based startup Goodfire just released a new tool, called Silico, that lets researchers and ...