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research-article

XAI tools in the public sector: a case study on predicting combined sewer overflows

Authors:

Nicholas Maltbie,

Matthew Van Doren,

Reese JohnsonAuthors Info & Claims

ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 1032 - 1044

https://doi.org/10.1145/3468264.3468547

Published: 18 August 2021 Publication History

Abstract

Artificial intelligence and deep learning are becoming increasingly prevalent in contemporary software solutions. Explainable artificial intelligence (XAI) tools attempt to address the black box nature of the deep learning models and make them more understandable to humans. In this work, we apply three state-of-the-art XAI tools in a real-world case study. Our study focuses on predicting combined sewer overflow events for a municipal wastewater treatment organization. Through a data driven inquiry, we collect both qualitative information via stakeholder interviews and quantitative measures. These help us assess the predictive accuracy of the XAI tools, as well as the simplicity, soundness, and insightfulness of the produced explanations. Our results not only show the varying degrees that the XAI tools meet the requirements, but also highlight that domain experts can draw new insights from complex explanations that may differ from their previous expectations.

References

[1]

Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. https://www.tensorflow.org/

[2]

Amina Adadi and Mohammed Berrada. 2018. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6 (2018), 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052

[3]

Saleema Amershi, Andrew Begel, Christian Bird, Robert DeLine, Harald C. Gall, Ece Kamar, Nachiappan Nagappan, Besmira Nushi, and Thomas Zimmermann. 2019. Software Engineering for Machine Learning: A Case Study. In Proceedings of the 41st IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP’19). Montreal, Canada. 291–300. https://doi.org/10.1109/ICSE-SEIP.2019.00042

Digital Library

[4]

Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015. On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation. PloS one, 10, 7 (2015), e0130140. https://doi.org/10.1371/journal.pone.0130140

[5]

José Manuel Benítez, Juan Luis Castro, and Ignacio Requena. 1997. Are Artificial Neural Networks Black Boxes? IEEE Transactions on Neural Networks, 8, 5 (1997), September, 1156–1164. https://doi.org/10.1109/72.623216

Digital Library

[6]

Tanmay Bhowmik, Vander Alves, and Nan Niu. 2014. An Exploratory Case Study on Exploiting Aspect Orientation in Mobile Game Porting. In Integration of Reusable Systems, Thouraya Bouabana-Tebibel and Stuart H. Rubin (Eds.). Springer, 241–261. https://doi.org/10.1007/978-3-319-04717-1_11

[7]

Supriyo Chakraborty, Richard Tomsett, Ramya Raghavendra, Daniel Harborne, Moustafa Alzantot, Federico Cerutti, Mani B. Srivastava, Alun D. Preece, Simon Julier, Raghuveer M. Rao, Troy D. Kelley, Dave Braines, Murat Sensoy, Christopher J. Willis, and Prudhvi Gurram. 2017. Interpretability of Deep Learning Models: A Survey of Results. In Proceedings of the IEEE International Conference on Ubiquitous Intelligence and Computing (UIC’17). San Francisco, CA, USA. 1–6. https://doi.org/10.1109/UIC-ATC.2017.8397411

[8]

Harshitha Challa, Nan Niu, and Reese Johnson. 2020. Faulty Requirements Made Valuable: On the Role of Data Quality in Deep Learning. In Proceedings of the 7th IEEE International Workshop on Artificial Intelligence for Requirements Engineering (AIRE’20). Zurich, Switzerland. 61–69. https://doi.org/10.1109/AIRE51212.2020.00016

[9]

Larissa Chazette and Kurt Schneider. 2020. Explainability as a Non-Functional Requirement: Challenges and Recommendations. Requirements Engineering, 25, 4 (2020), December, 493–514. https://doi.org/10.1007/s00766-020-00333-1

Digital Library

[10]

Lawrence Chung, Brian A. Nixon, Eric Yu, and John Mylopoulos. 1999. Non-Functional Requirements in Software Engineering. Springer.

[11]

Fabiano Dalpiaz and Nan Niu. 2020. Requirements Engineering in the Days of Artificial Intelligence. IEEE Software, 37, 4 (2020), July/August, 7–10. https://doi.org/10.1109/MS.2020.2986047

Digital Library

[12]

Hoa Khanh Dam, Truyen Tran, and Aditya Ghose. 2018. Explainable Software Analytics. In Proceedings of the 40th ACM/IEEE International Conference on Software Engineering: New Ideas and Emerging Results (ICSE’18). Gothenburg, Sweden. 53–56. https://doi.org/10.1145/3183399.3183424

Digital Library

[13]

Department for Environment Food & Rural Affairs. 2015. Creating a River Thames fit for our future: An updated strategic and economic case for the Thames Tideway Tunnel. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/471847/thames-tideway-tunnel-strategic-economic-case.pdf

[14]

DIVER. 2020. Web Application: Data Integration Visualization Exploration and Reporting Application, National Oceanic and Atmospheric Administration. https://www.diver.orr.noaa.gov

[15]

Finale Doshi-Velez and Been Kim. 2017. Towards A Rigorous Science of Interpretable Machine Learning. arxiv:1702.08608.

[16]

Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal. 2018. Explaining Explanations: An Overview of Interpretability of Machine Learning. In Proceedings of the 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA’18). Turin, Italy. 80–89. https://doi.org/10.1109/DSAA.2018.00018

[17]

Xavier Glorot and Yoshua Bengio. 2010. Understanding the Difficulty of Training Deep Feedforward Neural Networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS’10). Sardinia, Italy. 249–256. http://proceedings.mlr.press/v9/glorot10a.html

[18]

Bryce Goodman and Seth R. Flaxman. 2017. European Union Regulations on Algorithmic Decision-Making and a “Right to Explanation”. AI Magazine, 38, 3 (2017), 50–57. https://doi.org/10.1609/aimag.v38i3.2741

Digital Library

[19]

Hemanth Gudaparthi, Reese Johnson, Harshitha Challa, and Nan Niu. 2020. Deep Learning for Smart Sewer Systems: Assessing Nonfunctional Requirements. In Proceedings of the 42nd IEEE/ACM International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS’20). Seoul, South Korea. 35–38. https://dl.acm.org/doi/10.1145/3377815.3381379

[20]

Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2019. A Survey of Methods for Explaining Black Box Models. Comput. Surveys, 51, 5 (2019), January, 93:1–93:42. https://doi.org/10.1145/3236009

Digital Library

[21]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’16). Las Vegas, NV, USA. 770–778. https://doi.org/10.1109/CVPR.2016.90

[22]

High-Level Expert Group on Artificial Intelligence, European Commission. 2019. Policy and Investment Recommendations for Trustworthy AI. https://digital-strategy.ec.europa.eu/en/policies/expert-group-ai

[23]

Denis J. Hilton. 1990. Conversational Processes and Causal Explanation. Psychological Bulletin, 107, 1 (1990), January, 65–81. https://doi.org/10.1037/0033-2909.107.1.65

[24]

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, and John Grundy. 2021. Practitioners’ Perceptions of the Goals and Visual Explanations of Defect Prediction Models. arxiv:2102.12007.

[25]

Anna Jobin, Marcello Ienca, and Effy Vayena. 2019. The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1 (2019), September, 389–399. https://doi.org/10.1038/s42256-019-0088-2

[26]

Charu Khatwani, Xiaoyu Jin, Nan Niu, Amy Koshoffer, Linda Newman, and Juha Savolainen. 2017. Advancing Viewpoint Merging in Requirements Engineering: A Theoretical Replication and Explanatory Study. Requirements Engineering, 22, 3 (2017), September, 317–338. https://doi.org/10.1007/s00766-017-0271-0

Digital Library

[27]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. arxiv:1412.6980.

[28]

Maximilian A. Köhl, Kevin Baum, Markus Langer, Daniel Oster, Timo Speith, and Dimitri Bohlender. 2019. Explainability as a Non-Functional Requirement. In Proceedings of the 27th IEEE International Requirements Engineering Conference (RE’19). Jeju Island, South Korea. 363–368. https://doi.org/10.1109/RE.2019.00046

[29]

Tania Lombrozo. 2007. Simplicity and probability in causal explanation. Cognitive Psychology, 55, 3 (2007), November, 232–257. https://doi.org/10.1016/j.cogpsych.2006.09.006

[30]

Scott M Lundberg and Su-In Lee. 2017. A Unified Approach to Interpreting Model Predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). Long Beach, CA, USA. 4765–4774. http://papers.nips.cc/paper/6930-a-universal-analysis-of-large-scale-regularized-least-squares-solutions

Digital Library

[31]

Tim Miller, Piers Howe, and Liz Sonenberg. 2017. Explainable AI: Beware of Inmates Running the Asylum. arxiv:1712.00547v2.

[32]

Yao Ming, Huamin Qu, and Enrico Bertini. 2019. RuleMatrix: Visualizing and Understanding Classifiers with Rules. IEEE Transactions on Visualization and Computer Graphics, 25, 1 (2019), January, 342–352. https://doi.org/10.1109/TVCG.2018.2864812

Digital Library

[33]

Nan Niu, Sjaak Brinkkemper, Xavier Franch, Jari Partanen, and Juha Savolainen. 2018. Requirements Engineering and Continuous Deployment. IEEE Software, 35, 2 (2018), March/April, 86–90. https://doi.org/10.1109/MS.2018.1661332

[34]

Nan Niu and Steve Easterbrook. 2007. So, You Think You Know Others’ Goals? A Repertory Grid Study. IEEE Software, 24, 2 (2007), March/April, 53–61. https://doi.org/10.1109/MS.2007.52

Digital Library

[35]

Nan Niu, Amy Koshoffer, Linda Newman, Charu Khatwani, Chatura Samarasinghe, and Juha Savolainen. 2016. Advancing Repeated Research in Requirements Engineering: A Theoretical Replication of Viewpoint Merging. In Proceedings of the 24th IEEE International Requirements Engineering Conference (RE’16). Beijing, China. 186–195. https://doi.org/10.1109/RE.2016.46

[36]

Nan Niu, Alejandra Yepez Lopez, and Jing-Ru C. Cheng. 2011. Using Soft Systems Methodology to Improve Requirements Practices: An Exploratory Case Study. IET Software, 5, 6 (2011), December, 487–495. https://doi.org/10.1049/iet-sen.2010.0096

[37]

Nan Niu, Sandeep Reddivari, and Zhangji Chen. 2013. Keeping Requirements on Track via Visual Analytics. In Proceedings of the 21st IEEE International Requirements Engineering Conference (RE’13). Rio de Janeiro, Brazil. 205–214. https://doi.org/10.1109/RE.2013.6636720

[38]

Nan Niu, Li Da Xu, Jing-Ru C. Cheng, and Zhendong Niu. 2014. Analysis of Architecturally Significant Requirements for Enterprise Systems. IEEE Systems Journal, 8, 3 (2014), September, 850–857. https://doi.org/10.1109/JSYST.2013.2249892

[39]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12 (2011), 2825–2830. https://jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf

Digital Library

[40]

Sandeep Reddivari, Shirin Rad, Tanmay Bhowmik, Nisreen Cain, and Nan Niu. 2014. Visual Requirements Analytics: A Framework and Case Study. Requirements Engineering, 19, 3 (2014), September, 257–279. https://doi.org/10.1007/s00766-013-0194-3

Digital Library

[41]

Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016. 2016. General Data Protection Regulation. https://eur-lex.europa.eu/eli/reg/2016/679/oj

[42]

Alfréd Rényi. 1961. On Measures of Entropy and Information. In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics.

[43]

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016. “Why Should I Trust You?” Explaining the Predictions of Any Classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16). San Francisco, CA, USA. 1135–1144. https://doi.org/10.1145/2939672.2939778

Digital Library

[44]

Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017. Learning Important Features Through Propagating Activation Differences. arxiv:1704.02685.

[45]

Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arxiv:1312.6034v2.

[46]

Rini Van Solingen and Egon Berghout. 1999. The Goal/Question/Metric Method: A Practical Guide for Quality Improvement of Software Development. McGraw-Hill.

[47]

Gianni Talamini, Di Shao, X. Su, X. Guo, and X. Ji. 2016. Combined Sewer Overflow In Shenzhen, China: The Case Study of Dasha River. WIT Transactions on Ecology and the Environment, 210 (2016), 785–796. https://doi.org/10.2495/SDP160661

[48]

Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, and John Grundy. 2020. Explainable AI for Software Engineering. arxiv:2012.01614.

[49]

Paul Thagard. 1989. Explanatory Coherence. Behavioral and Brain Sciences, 12, 3 (1989), September, 435–502. https://doi.org/10.1017/S0140525X00057046

[50]

United States Environmental Protection Agency. 2004. Report to Congress: Impacts and control of CSOs and SSOs. https://www.epa.gov/npdes/2004-npdes-cso-report-congress

[51]

Wentao Wang, Nan Niu, Hui Liu, and Zhendong Niu. 2018. Enhancing Automated Requirements Traceability by Resolving Polysemy. In Proceedings of the 26th IEEE International Requirements Engineering Conference (RE’18). Banff, Canada. 40–51. https://doi.org/10.1109/RE.2018.00-53

[52]

Meredith Whittaker, Kate Crawford, Roel Dobbe, Genevieve Fried, Elizabeth Kaziunas, Varoon Mathur, Sarah Myers West, Rashida Richardson, Jason Schultz, and Oscar Schwartz. 2018. AI Now Report. https://ainowinstitute.org/AI_Now_2018_Report.pdf

[53]

Christine T. Wolf and Jeanette Blomberg. 2019. Evaluating the Promise of Human-Algorithm Collaborations in Everyday Work Practices. Proceedings of the ACM on Human-Computer Interaction, 3, EICS (2019), June, 143:1–143:23. https://doi.org/10.1145/3359245

Digital Library

[54]

Christine T. Wolf and Jeanette Blomberg. 2019. Explainability in Context: Lessons from an Intelligent System in the IT Services Domain. In Joint Proceedings of the ACM IUI 2019 Workshops (IUI’19). Los Angeles, CA, USA. http://ceur-ws.org/Vol-2327/IUI19WS-ExSS2019-17.pdf

[55]

Robert K. Yin. 2008. Case Study Research: Design and Methods. Sage Publications.

[56]

Jie M. Zhang, Mark Harman, Lei Ma, and Yang Liu. 2020. Machine Learning Testing: Survey, Landscapes and Horizons. IEEE Transactions on Software Engineering, https://doi.org/10.1109/TSE.2019.2962027

Digital Library

Cited By

Kim JMaathuis HSent D(2024)Human-centered evaluation of explainable AI applications: a systematic reviewFrontiers in Artificial Intelligence10.3389/frai.2024.14564867Online publication date: 17-Oct-2024
https://doi.org/10.3389/frai.2024.1456486
Wang ZHuang CYao X(2024)A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?ACM Transactions on Autonomous and Adaptive Systems10.1145/370200419:4(1-40)Online publication date: 24-Nov-2024
https://dl.acm.org/doi/10.1145/3702004
Weitz KSchlagowski RAndré EMänniste MGeorge C(2024)Explaining It Your Way - Findings from a Co-Creative Design Workshop on Designing XAI Applications with AI End-Users from the Public SectorProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642563(1-14)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642563
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Index Terms

XAI tools in the public sector: a case study on predicting combined sewer overflows
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Process validation

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cover image ACM Conferences

ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 2021

1690 pages

ISBN:9781450385626

DOI:10.1145/3468264

General Chairs:
Diomidis Spinellis
Athens University of Economics and Business, Greece
,
Georgios Gousios
Facebook, Netherlands / Delft University of Technology, Netherlands
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Massimiliano Di Penta
University of Sannio, Italy

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Published: 18 August 2021

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ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 23 - 28, 2021

Athens, Greece

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Cited By

Kim JMaathuis HSent D(2024)Human-centered evaluation of explainable AI applications: a systematic reviewFrontiers in Artificial Intelligence10.3389/frai.2024.14564867Online publication date: 17-Oct-2024
https://doi.org/10.3389/frai.2024.1456486
Wang ZHuang CYao X(2024)A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?ACM Transactions on Autonomous and Adaptive Systems10.1145/370200419:4(1-40)Online publication date: 24-Nov-2024
https://dl.acm.org/doi/10.1145/3702004
Weitz KSchlagowski RAndré EMänniste MGeorge C(2024)Explaining It Your Way - Findings from a Co-Creative Design Workshop on Designing XAI Applications with AI End-Users from the Public SectorProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642563(1-14)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642563
Bertrand AEagan JMaxwell WBrand J(2024)AI is Entering Regulated Territory: Understanding the Supervisors' Perspective for Model Justifiability in Financial Crime DetectionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642326(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642326
Dahiya MGill RNiu NGudaparthi HPeng Z(2024)Leveraging ChatGPT to Predict Requirements Testability with Differential In-Context Learning2024 IEEE International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI62200.2024.00044(170-175)Online publication date: 7-Aug-2024
https://doi.org/10.1109/IRI62200.2024.00044
Huang ZYu HFan GShao ZZhou ZLi M(2024)On the effectiveness of developer features in code smell prioritizationJournal of Systems and Software10.1016/j.jss.2024.111968210:COnline publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.111968
Yin ZSaadati YLeon AHadi Amini MBian LHu B(2024)Forecasting and optimization for minimizing combined sewer overflows using Machine learning frameworks and its inversion techniquesJournal of Hydrology10.1016/j.jhydrol.2023.130515628(130515)Online publication date: Jan-2024
https://doi.org/10.1016/j.jhydrol.2023.130515
Awadh MMallick J(2024)A decision-making framework for landfill site selection in Saudi Arabia using explainable artificial intelligence and multi-criteria analysisEnvironmental Technology & Innovation10.1016/j.eti.2023.10346433(103464)Online publication date: Feb-2024
https://doi.org/10.1016/j.eti.2023.103464
Huang ZYu HFan GShao ZLi MLiang Y(2024)Aligning XAI explanations with software developers’ expectationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121640238:PAOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121640
Shihadeh JAl-Shaibie GBisharah MAlshami DAlkhadrawi SAl-Bdour H(2024)Evaluation and prediction of time overruns in Jordanian construction projects using coral reefs optimization and deep learning methodsAsian Journal of Civil Engineering10.1007/s42107-023-00936-325:3(2665-2677)Online publication date: 9-Jan-2024
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