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Algorithms often appear as 'black boxes' to non-expert users. While priorwork focuses on explainable representations and expert-oriented exploration, wepropose and study an interactive approach using question answering to explaindeterministic algorithms to non-expert users who need to understand thealgorithms' internal states (e.g., students learning algorithms, operatorsmonitoring robots, admins troubleshooting network routing). We construct XAlgo-- a formal model that first classifies the type of question based on ataxonomy and generates an answer based on a set of rules that extractinformation from representations of an algorithm's internal states, e.g., thepseudocode. A design probe in an algorithm learning scenario with 18participants (9 for a Wizard-of-Oz XAlgo and 9 as a control group) reportsfindings and design implications based on what kinds of questions people ask,how well XAlgo responds, and what remain as challenges to bridge users' gulf ofunderstanding algorithms.

XAlgo: a Design Probe of Explaining Algorithms' Internal States via Question-Answering