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 …

While the interpretability of machine learning models is often equated with their mere
syntactic comprehensibility, we think that interpretability goes beyond that, and that human …

Several strands of research have aimed to bridge the gap between artificial intelligence (AI)
and human decision-makers in AI-assisted decision-making, where humans are the …

Why are human inferences sometimes remarkably close to the Bayesian ideal and other
times systematically biased? In particular, why do humans make near-rational inferences in …

The ability to act on the world with the goal of gaining information is core to human
adaptability and intelligence. Perhaps the most successful and influential account of such …

Likelihood ratios are increasingly being adopted to convey expert evaluative opinions to
courts. In the absence of appropriate databases, many of these likelihood ratios will include …

It is conventional wisdom in machine learning and data mining that logical models such as
rule sets are more interpretable than other models, and that among such rule-based models …

Standards published by the Association of Forensic Science Providers (2009, Standards for
the formulation of evaluative forensic science expert opinion, Science & Justice, Vol. 49, pp …

Causal graphical models (CGMs) are a popular formalism used to model human causal
reasoning and learning. The key property of CGMs is the causal Markov condition, which …

Forensic scientists and commentators including academics and statisticians have been
embroiled in a debate over the best way to present evidence in the courtroom. Various forms …