Interpretability in deep learning for finance: A case study for the Heston model

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...
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

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 ...
Reuters

Dan Herbatschek Leads New Initiative in New York and Los Angeles to Advance Transparency ...

NEW YORK, NY, November 28, 2025 (EZ Newswire) -- Dan Herbatschek, opens new tab, founder and CEO of Ramsey Theory Group, opens new tab, has announced a new initiative to strengthen transparency, ...
The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
Scientific Research Publishing

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using ...

A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Frontiers

Brain-Computer Interfaces (BCIs) for daily activities: Innovations in EEG signal analysis ...

Brain-Computer Interfaces (BCIs) are emerging as transformative tools that enable direct communication between the human brain and external devices. With recent advancements in Electroencephalography ...