It has long been hypothesised that causal reasoning plays a fundamental role in robust and
general intelligence. However, it is not known if agents must learn causal models in order to …

There is a fast-growing literature in addressing the fairness of AI models (fair-AI), with a
continuous stream of new conceptual frameworks, methods, and tools. How much can we …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …

Addressing the problem of fairness is crucial to safely using machine learning algorithms to
support decisions that have a critical impact on people's lives, such as job hiring, child …

This chapter focuses on the role of machine learning in cyber-physical systems to improve
manufacturing processes. With the increasing use of automation in manufacturing, there is a …

Besides its common use cases in epidemiology, political, and social sciences, causality
turns out to be crucial in evaluating the fairness of automated decisions, both in a legal and …

Fairness in clinical decision-making is a critical element of health equity, but assessing
fairness of clinical decisions from observational data is challenging. Recently, many fairness …

How can we teach machine learning to identify causal patterns in data? This book explores
the very notion of" causality", identifying from a naturalistic and evolutionary perspective how …

In the relentless pursuit of advancing artificial intelligence (AI) to emulate human cognition, a
crucial frontier lies in achieving a genuine comprehension of causality. This chapter …

Causal reasoning has an indispensable role in how humans make sense of the world and
come to decisions in everyday life. While 20th century science was reserved from making …