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Investigating the effects of (good) explanations and the explainer’s reliability on our beliefs in what is being explained.

We bring together two closely related, but distinct, notions: argument and explanation. We provide a review of relevant research on these notions, drawn both from the cognitive science and the artificial intelligence (AI) literatures. We identify key directions for future research, indicating areas where bringing together cognitive science and AI perspectives would be mutually beneficial.

We explore some of the undesirable effects of providing explanations of AI systems to human users and ways to mitigate such effects. We show how providing counterfactual explanations of AI systems’ predictions unjustifiably changes people’s beliefs about causal relationships in the real world. We also show how health warning style messaging can prevent such a change in beliefs.

Empirical testing of the effects of the propensity interpretation of probability and ‘diagnostic split’ reasoning in the context of explaining away.

An experimental exploration of whether a Bayesian network modeling tool helps lay people to find correct solutions to complex problems.

A justification for Inference to the Best Explanation (IBE) is provided by identifying conditions under which the best explanation of evidence can offer a confirmatory boost to the hypotheses under consideration.

Analyzing confirmation between theories in cases of intertheoretic reduction (e.g. reducing thermodynamics to statistical mechanics) using Bayesian networks.