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 Heston option pricing model as a benchmark, researchers show that global ...
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 ...
ascopubs.org

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering ...

PROSPeCT: A Predictive Research Online System for Prostate Cancer Tasks Time to event is an important aspect of clinical decision making. This is particularly true when diseases have highly ...
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 ...
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 ...
Microsoft

Microsoft responsible machine learning capabilities build trust in AI systems, developers say

Anyone who runs a business knows that one of the hardest things to do is accuse a customer of malfeasance. That’s why, before members of Scandinavian Airlines’ (SAS) fraud detection unit accuse a ...
Quanta Magazine

The Computer Scientist Peering Inside AI’s Black Boxes

Machine learning models are incredibly powerful tools. They extract deeply hidden patterns in large data sets that our limited human brains can’t parse. These complex algorithms, then, need to be ...