Abstract
An automated explanation facility for Bayesian conditioning aimed at improving user acceptance of probability-based decision support systems has been developed. The domain-independent facility is based on an information processing perspective on reasoning about conditional evidence that accounts both for biased and normative inferences. Experimental results indicate that the facility is both acceptable to naive users and effective in improving understanding.
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