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A Rubric for Implementing Explainable AI in Production Logistics

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Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action (APMS 2022)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 663))

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Abstract

With the advent of Industry 4.0, the world is witnessing increasing use of data and data-driven services. This phenomenon has penetrated through different sectors of production including logistics. The purpose of this study is to explore the use of Artificial Intelligence (AI) and Machine Learning (ML) in production logistics. This paper is the first step in the direction of understanding the complexity of AI and ML algorithms and thus explaining the black-box-like characteristics of these algorithms. This is coupled with the definition of eXplainable AI (XAI) in the domain. The paper furthers describes the needs for XAI and consequently presents a rubric for implementing XAI in the domain of production logistics and discusses it in detail.

Supported by Vinnova funded project EXPLAIN.

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References

  1. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access (2018)

    Google Scholar 

  2. Ahmad, M.A., Eckert, C., Teredesai, A.: Interpretable machine learning in healthcare. In: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (2018)

    Google Scholar 

  3. Andrews, R., Diederich, J., Tickle, A.B.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl. Based Syst. (1995)

    Google Scholar 

  4. Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K.: Explainable agents and robots: results from a systematic literature review. In: 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada, 13–17 May 2019. International Foundation for Autonomous Agents and Multiagent Systems (2019)

    Google Scholar 

  5. Antoniadi, A.M., et al.: Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Appl. Sci. 11(11) (2021)

    Google Scholar 

  6. Arbatli, A.D., Akin, H.L.: Rule extraction from trained neural networks using genetic algorithms. Nonlinear Anal. Theory Methods Appl. (1997)

    Google Scholar 

  7. Barocas, S., Selbst, A.D.: Big data’s disparate impact. Calif. Law Rev. 104, 671 (2016)

    Google Scholar 

  8. Berente, N., Gu, B., Recker, J., Santhanam, R.: Managing Ai. MIS Q. (2019). Call for Papers

    Google Scholar 

  9. Binns, R.: Fairness in machine learning: lessons from political philosophy. In: Conference on Fairness, Accountability and Transparency. PMLR (2018)

    Google Scholar 

  10. Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., Elhadad, N.: Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015)

    Google Scholar 

  11. De Montjoye, Y.A., Radaelli, L., Singh, V.K., Pentland, A.S.: Unique in the shopping mall: on the reidentifiability of credit card metadata. Science (2015)

    Google Scholar 

  12. Dieterich, W., Mendoza, C., Brennan, T.: Demonstrating accuracy equity and predictive parity performance of the compas risk scales in broward county (2016)

    Google Scholar 

  13. Dua, S., Acharya, U.R., Dua, P.: Machine Learning in Healthcare Informatics. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-40017-9

  14. Esteva, A., et al.: A guide to deep learning in healthcare. Nat. Med. (2019)

    Google Scholar 

  15. Fürnkranz, J., Kliegr, T., Paulheim, H.: On cognitive preferences and the plausibility of rule-based models. Mach. Learn. 109(4) (2020)

    Google Scholar 

  16. Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA). IEEE (2018)

    Google Scholar 

  17. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box models. ACM Comput. Surv. (CSUR) (2018)

    Google Scholar 

  18. Hajian, S., Domingo-Ferrer, J.: A methodology for direct and indirect discrimination prevention in data mining. IEEE Trans. Knowl. Data Eng. (2012)

    Google Scholar 

  19. Howard, A., Zhang, C., Horvitz, E.: Addressing bias in machine learning algorithms: a pilot study on emotion recognition for intelligent systems. In: 2017 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO). IEEE (2017)

    Google Scholar 

  20. Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science (2015)

    Google Scholar 

  21. Khandani, A.E., Kim, A.J., Lo, A.W.: Consumer credit-risk models via machine-learning algorithms. J. Bank. Financ. (2010)

    Google Scholar 

  22. Klumpp, M., Hesenius, M., Meyer, O., Ruiner, C., Gruhn, V.: Production logistics and human-computer interaction-state-of-the-art, challenges and requirements for the future. Int. J. Adv. Manuf. Technol. 105(9) (2019)

    Google Scholar 

  23. Knoll, D., Prüglmeier, M., Reinhart, G.: Predicting future inbound logistics processes using machine learning. Procedia CIRP (2016)

    Google Scholar 

  24. Kusner, M.J., Loftus, J., Russell, C., Silva, R.: Counterfactual fairness. Adv. Neural Inf. Process. Syst. (2017)

    Google Scholar 

  25. Layard, P.R.G., et al.: Cost-Benefit Analysis. Cambridge University Press, Cambridge (1994)

    Google Scholar 

  26. Le, H.H., Viviani, J.L.: Predicting bank failure: an improvement by implementing a machine-learning approach to classical financial ratios. Res. Int. Bus. Financ. (2018)

    Google Scholar 

  27. Linardatos, P., Papastefanopoulos, V., Kotsiantis, S.: Explainable ai: a review of machine learning interpretability methods. Entropy 23, 18 (2020)

    Google Scholar 

  28. Markowska-Kaczmar, U., Wnuk-Lipiński, P.: Rule extraction from neural network by genetic algorithm with pareto optimization. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 450–455. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24844-6_66

    Chapter  Google Scholar 

  29. Meske, C., Bunde, E., Schneider, J., Gersch, M.: Explainable artificial intelligence: objectives, stakeholders, and future research opportunities. Inf. Syst. Manag. (2022)

    Google Scholar 

  30. Nagy, G., Illés, B., Bányai, Á.: Impact of industry 4.0 on production logistics. In: IOP Conference Series: Materials Science and Engineering, vol. 448. IOP Publishing (2018)

    Google Scholar 

  31. Nazar, M., Alam, M.M., Yafi, E., Mazliham, M.: A systematic review of human-computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access (2021)

    Google Scholar 

  32. Nyhuis, P., Wiendahl, H.P.: Fundamentals of Production Logistics: Theory, Tools and Applications. Springer Science & Business Media, Heidelberg (2008). https://doi.org/10.1007/978-3-540-34211-3

  33. Panigutti, C., Perotti, A., Pedreschi, D.: Doctor XAI: an ontology-based approach to black-box sequential data classification explanations. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (2020)

    Google Scholar 

  34. Rai, A., Constantinides, P., Sarker, S.: Next generation digital platforms: toward human-ai hybrids. Mis Q. (2019)

    Google Scholar 

  35. Ras, G., van Gerven, M., Haselager, P.: Explanation methods in deep learning: users, values, concerns and challenges. In: Escalante, H.J., et al. (eds.) Explainable and Interpretable Models in Computer Vision and Machine Learning. TSSCML, pp. 19–36. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98131-4_2

    Chapter  Google Scholar 

  36. Ravì, D., et al.: Deep learning for health informatics. IEEE J. Biomed. Health Inform. (2016)

    Google Scholar 

  37. Ribeiro, M.T., Singh, S., Guestrin, C.: 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 (2016)

    Google Scholar 

  38. Singh, A., Wiktorsson, M., Hauge, J.B.: Trends in machine learning to solve problems in logistics. Procedia CIRP (2021)

    Google Scholar 

  39. Stepin, I., Alonso, J.M., Catala, A., Pereira-Fariña, M.: A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence. IEEE Access 9 (2021)

    Google Scholar 

  40. Strandhagen, J.W., Alfnes, E., Strandhagen, J.O., Vallandingham, L.R.: The fit of industry 4.0 applications in manufacturing logistics: a multiple case study. Adv. Manuf. 5(4) (2017)

    Google Scholar 

  41. Wang, N., Pynadath, D.V., Hill, S.G.: Trust calibration within a human-robot team: comparing automatically generated explanations. In: 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE (2016)

    Google Scholar 

  42. Woschank, M., Rauch, E., Zsifkovits, H.: A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics. Sustainability (2020)

    Google Scholar 

  43. Wu, S.D., Roundy, R.O., Storer, R.H., Martin-Vega, L.A.: Manufacturing logistics research: taxonomy and directions. Technical report, Cornell University Operations Research and Industrial Engineering(1999)

    Google Scholar 

  44. Zilke, J.R., Loza Mencía, E., Janssen, F.: DeepRED–rule extraction from deep neural networks. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 457–473. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46307-0_29

    Chapter  Google Scholar 

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Authors and Affiliations

  1. KTH Royal Institute of Technology, Södertälje 151 36, Stockholm, Sweden

    Amita Singh, Erik Flores Garcia, Yongkuk Jeong & Magnus Wiktorsson

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  1. Amita Singh
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  2. Erik Flores Garcia
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  3. Yongkuk Jeong
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  4. Magnus Wiktorsson
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Correspondence to Amita Singh .

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Editors and Affiliations

  1. Pohang University of Science and Technology, Pohang, Korea (Republic of)

    Duck Young Kim

  2. ZF Friedrichshafen AG, Friedrichshafen, Germany

    Gregor von Cieminski

  3. Tecnológico de Monterrey, Mexico City, Mexico

    David Romero

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Singh, A., Garcia, E.F., Jeong, Y., Wiktorsson, M. (2022). A Rubric for Implementing Explainable AI in Production Logistics. In: Kim, D.Y., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action. APMS 2022. IFIP Advances in Information and Communication Technology, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-031-16407-1_23

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  • DOI: https://doi.org/10.1007/978-3-031-16407-1_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16406-4

  • Online ISBN: 978-3-031-16407-1

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