Explanation of Probabilistic Inference for Decision Support Systems

TL;DR

This paper presents an explanation system for Bayesian decision support that enhances user understanding and acceptance by clarifying probabilistic reasoning processes.

Contribution

It introduces a domain-independent explanation facility for Bayesian inference that improves user comprehension and acceptance in decision support systems.

Findings

The explanation facility is accepted by naive users.

It effectively improves understanding of probabilistic reasoning.

The system is based on an information processing perspective.

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.