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 …

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

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 …

Feature attributions and counterfactual explanations are popular approaches to explain a
ML model. The former assigns an importance score to each input feature, while the latter …

Accounts of scientific explanation have been a major focus of discussion in philosophy of
science for many decades. It is a presupposition of such accounts that science aims at (and …

This paper attempts to elucidate three characteristics of causal relationships that are
important in biological contexts. Stability has to do with whether a causal relationship …

To improve upon their current knowledge, learners must be able to generate informative
data and accurately evaluate this evidence. However, there is substantial disagreement …

An essential introduction to the philosophy of biology This is a concise, comprehensive, and
accessible introduction to the philosophy of biology written by a leading authority on the …

The past few decades have seen an explosion of research on causal reasoning in
philosophy, computer science, and statistics, as well as descriptive work in psychology. In …

While a vast collection of explainable AI (XAI) algorithms has been developed in recent
years, they have been criticized for significant gaps with how humans produce and consume …