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The need of predictive maintenance comes with an increasing number ofincidents reported by monitoring systems and equipment/software users. In thefront line, on-call engineers (OCEs) have to quickly assess the degree ofseverity of an incident and decide which service to contact for correctiveactions. To automate these decisions, several predictive models have beenproposed, but the most efficient models are opaque (say, black box), stronglylimiting their adoption. In this paper, we propose an efficient black box modelbased on 170K incidents reported to our company over the last 7 years andemphasize on the need of automating triage when incidents are massivelyreported on thousands of servers running our product, an ERP. Recentdevelopments in eXplainable Artificial Intelligence (XAI) help in providingglobal explanations to the model, but also, and most importantly, with localexplanations for each model prediction/outcome. Sadly, providing a human withan explanation for each outcome is not conceivable when dealing with animportant number of daily predictions. To address this problem, we propose anoriginal data-mining method rooted in Subgroup Discovery, a pattern miningtechnique with the natural ability to group objects that share similarexplanations of their black box predictions and provide a description for eachgroup. We evaluate this approach and present our preliminary results which giveus good hope towards an effective OCE's adoption. We believe that this approachprovides a new way to address the problem of model agnostic outcomeexplanation.

Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery