Human-Machine Diversity in the Use of Computerised Advisory Systems: A Case Study

Computer-based advisory systems form with their users composite, human-machine systems. Redundancy and diversity between the human and the machine are often important for the dependability of such systems. We describe a case study on assessing failure probabilities for the analysis of X-ray films for detecting cancer, performed by a person assisted by a computerbased tool. Differently from most approaches to human reliability assessment, we focus on the effects of failure diversity – or correlation – between humans and machines. We illustrate some of the modelling and prediction problems, especially those caused by the presence of the human component. We show two alternative models, with their pros and cons, and illustrate, via numerical examples and analytically, some interesting and non-intuitive answers to questions about reliability assessment and design choices for human-computer systems. Copyright of the authors, 2002 This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.