  Algorithms often appear as 'black boxes' to non-expert users. While prior
work focuses on explainable representations and expert-oriented exploration, we
propose and study an interactive approach using question answering to explain
deterministic algorithms to non-expert users who need to understand the
algorithms' internal states (e.g., students learning algorithms, operators
monitoring robots, admins troubleshooting network routing). We construct XAlgo
-- a formal model that first classifies the type of question based on a
taxonomy and generates an answer based on a set of rules that extract
information from representations of an algorithm's internal states, e.g., the
pseudocode. A design probe in an algorithm learning scenario with 18
participants (9 for a Wizard-of-Oz XAlgo and 9 as a control group) reports
findings and design implications based on what kinds of questions people ask,
how well XAlgo responds, and what remain as challenges to bridge users' gulf of
understanding algorithms.
