Evaluating alerting systems from descriptions
Authors: Meyer Joachim 
Published: September 2017

Decisions in almost all domains of life receive support from automation in the form of alerts, binary cues, recommendations, etc. People often use automation or decision aids without having experience with the system, because the system may be new or because they rarely use it. When such experience is unavailable, people will base their use of the system on information they may have received about it and on descriptions, often given as probabilities or proportions. Examples are the sensitivity and specificity of a diagnostic procedure in medicine or the True Positive and False Positive rates of a detector. People use these descriptions to decide to what extent they can rely on the information. So far, it is unclear which aspects of the information about a system determine people’s evaluation of the system from a description. These evaluations will determine the trust they put in the indications from the system and the adjustment of system properties, such as thresholds. To gain some insights into this issue, we conducted an experiment. We developed descriptions of 12 systems in a quality control setting, in which participants had to detect faulty items in a production process. We used Signal Detection Theory (Green & Swets, 1966) to determine the system properties. The systems differed in d’ (1.5 or 2.5), the threshold setting lnβ (-1, 0 or 1) and the prior probability for a signal pS (.05 or .2). Half of the participants saw diagnostic values, receiving descriptions in terms of the probabilities of Hit and False Alarms, while the other half saw descriptions as predictive values, receiving the Positive Predictive Value (PPV) and the Negative Predictive Value (NPV) of each system. In the past, we have shown that people adjust system thresholds better when they see predictive values (Botzer, Meyer, Bak, & Parmet, 2010). Fifty-six students evaluated the systems in a classroom setting on a scale between 0 (completely useless) and 10 (perfect). In addition to the d’ and lnβ, which we specified when we designed the systems, we also computed for each system the Probability of Correct Indication (pCorrect), the Expected Value (given the costs and benefits in the description), and the transmitted information according to Information Theory. We analyzed the results with multivariate analyses of variance and by computing the correlations between the evaluations and system properties. The results showed that participants’ responses were mainly correlated with d’. The effects of the threshold setting lnβ and of pS were small, compared to the effects of d’. The correlations with the Expected Value and the transmitted information were smaller and could be explained through d’. Thus, people evaluated a system in terms of its ability to differentiate between signal and noise. They did not evaluate the system according to the economic value it provided or the transmitted information. In addition, participants evaluated systems with different thresholds (lnβ) similarly. This means that in our experiment participants did not differentiate between more and less appropriate threshold settings. The ability to identify better or worse settings is important, because these settings are often the main system parameter users can adjust. These findings, in addition to the inherent problems that already exist in user adjustments of systems (Meyer & Sheridan, 2017), make it unlikely that people can adjust system settings correctly.