There has been a recent resurgence in the area of explainable artificial intelligence as
researchers and practitioners seek to provide more transparency to their algorithms. Much of …

Causal knowledge plays a crucial role in human thought, but the nature of causal
representation and inference remains a puzzle. Can human causal inference be captured by …

The causal capabilities of large language models (LLMs) are a matter of significant debate,
with critical implications for the use of LLMs in societally impactful domains such as …

An influential line of thinking in behavioral science, to which the two authors have long
subscribed, is that many of society's most pressing problems can be addressed cheaply and …

Algorithmic systems that employ machine learning play an increasing role in making
substantive decisions in modern society, ranging from online personalization to insurance …

A new approach for defining causality and such related notions as degree of responsibility,
degrees of blame, and causal explanation. Causality plays a central role in the way people …

We argue that explainable artificial intelligence (XAI), specifically reason-giving XAI, often
constitutes the most suitable way of ensuring that someone can properly be held …

We propose a new definition of actual causes, using structural equations to model
counterfactuals. We show that the definition yields a plausible and elegant account of …

Ensuring the trustworthiness of autonomous systems and artificial intelligence is an
important interdisciplinary endeavour. In this position paper, we argue that this endeavour …

The original Halpern-Pearl definition of causality [Halpern and Pearl, 2001] was updated in
the journal version of the paper [Halpern and Pearl, 2005] to deal with some problems …