%0 Conference Paper %1 Rebanal_2021 %A Rebanal, Juan %A Combitsis, Jordan %A Tang, Yuqi %A Chen, Xiang \textquotesingleAnthony\textquotesingle %B 26th International Conference on Intelligent User Interfaces %D 2021 %I ACM %K explainable-AI explanation iui2021 %P 329-339 %R 10.1145/3397481.3450676 %T XAlgo: a Design Probe of Explaining Algorithms' Internal States via Question-Answering %U https://doi.org/10.1145%2F3397481.3450676 %X 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 (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, the pseudocode. A design probe based on 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 algorithm understanding.

@inproceedings{Rebanal_2021, abstract = {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 (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, the pseudocode. A design probe based on 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 algorithm understanding.}, added-at = {2022-01-16T20:47:39.000+0100}, author = {Rebanal, Juan and Combitsis, Jordan and Tang, Yuqi and Chen, Xiang {\textquotesingle}Anthony{\textquotesingle}}, biburl = {https://www.bibsonomy.org/bibtex/231a908c5cfe049ed42ed1c45adfb6e64/brusilovsky}, booktitle = {26th International Conference on Intelligent User Interfaces}, description = {XAlgo: a Design Probe of Explaining Algorithms’ Internal States via Question-Answering | 26th International Conference on Intelligent User Interfaces}, doi = {10.1145/3397481.3450676}, interhash = {ac60b54c98aa3296aa34319d6164830d}, intrahash = {31a908c5cfe049ed42ed1c45adfb6e64}, keywords = {explainable-AI explanation iui2021}, month = apr, pages = {329-339}, publisher = {{ACM}}, timestamp = {2022-01-16T20:47:39.000+0100}, title = {{XAlgo}: a Design Probe of Explaining Algorithms' Internal States via Question-Answering}, url = {https://doi.org/10.1145%2F3397481.3450676}, year = 2021 }