Skip to main content
SpringerLink
Log in

Interpretability of Machine Learning Solutions in Industrial Decision Engineering

  • Conference paper
  • First Online:
Data Mining (AusDM 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1127))

Included in the following conference series:

Abstract

The broad application of machine learning (ML) methods and algorithms in diverse range of organisational settings led to the adoption of legislation, like European Union’s General Data Protection Regulation, which require firm capabilities to explain algorithmic decisions. Currently in the ML literature there does not seem to be a consensus on the definition of interpretability of a ML solution. Moreover, there is no agreement about the necessary level of interpretability of such solution and on how this level can be determined, measured and achieved. In this article, we provide such definitions based on research as well as our extensive experience of building ML solutions for various organisations across industries. We present CRISP-ML, a detailed step-by-step methodology, that provides guidance on creating the necessary level of interpretability at each stage of the solution building process and is consistent with the best practices of project management in the ML settings. We illustrate the versatility and effortless applicability of CRISP-ML with examples across a variety of industries and types of ML projects.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Big Data and AI executive survey. Technical report, NewVantagePartners LLC (2019)

    Google Scholar 

  2. General Data Protection Regulation (GDPR). Official Journal of the European Union L 119/1 (2016). https://gdpr-info.eu/

  3. PMBOK® Guide - Sixth Edition. Project Magament Institute (2017)

    Google Scholar 

  4. Google AI: Responsible AI Practices - Interpretability. https://ai.google/responsibilities/responsible-ai-practices/?category=interpretability. Accessed 5 Aug 2019

  5. Ahmed, B., Dannhauser, T., Philip, N.: A Lean Design Thinking Methodology (LDTM) for machine learning and modern data projects. In: Proceedings of 2018 10th Computer Science and Electronic Engineering (CEEC), pp. 11–14. IEEE (2018)

    Google Scholar 

  6. Dawson, D., et al.: Artificial intelligence: Australia’s ethics framework. Technical report, Data61 CSIRO, Australia (2019)

    Google Scholar 

  7. Doshi-Velez, F., Kim, B.: Towards a rigorous science of interpretable machine learning. arXiv e-prints arXiv:1702.08608, February 2017

  8. Fleming, O., Fountaine, T., Henke, N., Saleh, T.: Ten red flags signaling your analytics program will fail. Technical report, McKinsey & Company (2018)

    Google Scholar 

  9. Gilpin, L.H., Testart, C., Fruchter, N., Adebayo, J.: Explaining explanations to society. CoRR abs/1901.06560 (2019). http://arxiv.org/abs/1901.06560

  10. Gleicher, M.: A framework for considering comprehensibility in modeling. Big Data 4(2), 75–88 (2016)

    Article  Google Scholar 

  11. Goodson, M.: Reasons why data projects fail. KDnuggets, November 2016. https://www.kdnuggets.com/2016/11/ten-ways-data-project-fail.html

  12. Grady, N.W., Underwood, M., Roy, A., Chang, W.L.: Big data: challenges, practices and technologies: In: NIST Big Data Public Working Group workshop at IEEE Big Data 2014. Proceedings of IEEE International Conference on Big Data 2014, pp. 11–15 (2014)

    Google Scholar 

  13. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. 51(5), 93:1–93:42 (2018)

    Article  Google Scholar 

  14. Huang, W., McGregor, C., James, A.: A comprehensive framework design for continuous quality improvement within the neonatal intensive care unit: integration of the SPOE, CRISP-DM and PaJMa models. In: Proceedings of IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 289–292 (2014)

    Google Scholar 

  15. Larson, D., Chang, V.: A review and future direction of agile, business intelligence, analytics and data science. Int. J. Inf. Manag. 36(5), 700–710 (2016)

    Article  Google Scholar 

  16. Lipton, Z.C.: The mythos of model interpretability. ACM Queue 16(3), 30:31–30:57 (2018)

    MathSciNet  Google Scholar 

  17. Mariscal, G., Marbán, O., Fernández, C.: A survey of data mining and knowledge discovery process models and methodologies. Knowl. Eng. Rev. 25(2), 137–166 (2010)

    Article  Google Scholar 

  18. Molnar, C., Casalicchio, G., Bischl, B.: Quantifying interpretability of arbitrary machine learning models through functional decomposition. arXiv:1904.03867

  19. Murdoch, W.J., Singh, C., Kumbier, K., Abbasi-Asl, R., Yu, B.: Interpretable machine learning: definitions, methods, and applications. arXiv:1901.04592

  20. O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishers, New York (2016)

    MATH  Google Scholar 

  21. Plotnikova, V.: Towards a data mining methodology for the banking domain. In: Kirikova, M., et al. (ed.) Proceedings of the Doctoral Consortium Papers Presented at the 30th International Conference on Advanced Information Systems Engineering, CAiSE 2018, pp. 46–54 (2018)

    Google Scholar 

  22. Ransbotham, S., Kiron, D., Prentice, P.K.: Minding the analytics gap. MIT Sloan Manag. Rev. 56, 1 (2015)

    Google Scholar 

  23. Reisman, D., Schultz, J., Crawford, K., Whittaker, M.: Algorithmic impact assessments: a practical framework for public agency accountability. Technical report, AI Now Institute, April 2018

    Google Scholar 

  24. Roy Schulte, W., et al.: Predicts 2019: data and analytics strategy. Technical report, Gartner Research, November 2018

    Google Scholar 

  25. Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019)

    Article  Google Scholar 

  26. Saltz, J.S., Shamshurin, I.: Big data team process methodologies: a literature review and the identification of key factors for a project’s success. In: Proceedings of 2016 IEEE International Conference on Big Data 2016, pp. 2872–2879 (2016)

    Google Scholar 

  27. Saltz, J.S., Shamshurin, I., Crowston, K.: Comparing data science project management methodologies via a controlled experiment. In: HICSS (2017)

    Google Scholar 

  28. Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Warehouse. 5, 13–22 (2000)

    Google Scholar 

  29. Stieglitz, C.: Beginning at the end - requirements gathering lessons from a flowchart junkie. In: PMI® Global Congress 2012–North America, Vancouver, British Columbia, Canada. Project Management Institute, Newtown Square, PA (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Analytikk Consulting, Sydney, Australia

    Inna Kolyshkina

  2. Western Sydney University, Penrith, NSW, 2751, Australia

    Simeon Simoff

Authors
  1. Inna Kolyshkina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simeon Simoff
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Inna Kolyshkina .

Editor information

Editors and Affiliations

  1. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

    Thuc D. Le

  2. La Trobe University, Melbourne, Australia

    Kok-Leong Ong

  3. CSIRO Scientific Computing, Canberra, Australia

    Yanchang Zhao

  4. CSIRO Scientific Computing, Canberra, Australia

    Warren H. Jin

  5. Consilium Technology, Adelaide, Australia

    Sebastien Wong

  6. School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, Australia

    Lin Liu

  7. Microsoft Proprietary Limited, Singapore, Singapore

    Graham Williams

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kolyshkina, I., Simoff, S. (2019). Interpretability of Machine Learning Solutions in Industrial Decision Engineering. In: Le, T., et al. Data Mining. AusDM 2019. Communications in Computer and Information Science, vol 1127. Springer, Singapore. https://doi.org/10.1007/978-981-15-1699-3_13

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-1699-3_13

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1698-6

  • Online ISBN: 978-981-15-1699-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation