A Guided Tour of Artificial Intelligence Research

Volume I: Knowledge Representation, Reasoning and Learning Introduction The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues....

Pearl, Judea and Mackenzie, Dana: The book of why: the new science of cause and effect (2018)

Basic Books, New York. ISBN 9780465097609 (hardcover) Karamjit S. Gill  [email protected] University of Brighton, Brighton, UK © Springer-Verlag London Ltd., part of Springer Nature 2020 ‘The Book of Why’ by Judea Pearl and Dana Mackenzie...

A New Social Contract in the Age of AI: Protection of Privacy Rights in the Times of COVID-19

World Leadership Alliance-Club de Madrid and Boston Global Forum co-organized this Roundtable at 9:00 am EST, May 12, 2020 Speakers: María Elena Agüero, Secretary General of the Club De Madrid Nguyen Anh Tuan, Co-founder and CEO of Boston Global Forum Professor...

What are the differences between econometrics, statistics, and machine learning?

In comparing econometrics, statistics, and machine learning methodologies, one must distinguish between standard and advanced machine learning. The former, exemplified by deep learning and neural networks, fits a function to a stream of data and plays...

On The Reasons Behind Decisions

Adnan Darwiche, Auguste Hirth Professor Judea Pearl commented “Speaking about “explainable AI”, this paper shows that, even in classification tasks, and even after agreeing on a Bayesian Network classifier, answering “why”...

AIWS Summit 2020: The Chief of the United Nations Academic Impact’s talk

Ramu Damodaran is the chief of the United Nations Academic Impact , an initiative he was responsible for designing ten years ago and which now has more than 1300 member institutions. He also oversees the civil society and advocacy outreach in the United...

Causal Inference in Machine Learning and AI

Online lecture on causal data science wtih directed acyclic graphs (DAGs) in the advanced applied econometrics course at KU Leuven, Belgium (May 7, 2020). Talk is based on the following paper: https://arxiv.org/abs/1912.09104 Further teaching material:...

Causal Inference Without Counterfactuals: Comment

Judea Pearl 1. BACKGROUND The field of statistics has seen many well-meaning crusades against threats from metaphysics and other heresy. In its founding prospectus of 1834, the Royal Statistical Society resolved “to exclude carefully all Opinions...

Decision-theoretic foundations for statistical causality

A. Philip Dawid We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic statistical causality (DT), which is a straightforward way of representing and addressing causal questions. DT reframes causal inference as...