Explanation of Probabilistic Inference for Decision Support Systems
This work addresses the challenge of making probabilistic inference more accessible for users in decision support systems, though it appears incremental as it builds on existing Bayesian methods.
The paper tackled the problem of user acceptance and understanding in probability-based decision support systems by developing an automated explanation facility for Bayesian conditioning, which was found to be acceptable to naive users and effective in improving understanding.
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.