Abstract
With the advent of digitization on the shopfloor and the developments of Industry 4.0, companies are faced with opportunities and challenges alike. This can be illustrated by the example of AI-based process predictions, which can be valuable for real-time process management in a smart factory. However, to constructively collaborate with such a prediction, users need to establish confidence in its decisions. Explainable artificial intelligence (XAI) has emerged as a new research area to enable humans to understand, trust, and manage the AI they work with. In this contribution, we illustrate the opportunities and challenges of process predictions and XAI for Industry 4.0 with the DFKI-Smart-Lego-Factory. This fully automated factory prototype built out of LEGO\(^\circledR\) bricks demonstrates the potentials of Industry 4.0 in an innovative, yet easily accessible way. It includes a showcase that predicts likely process outcomes and uses state-of-the-art XAI techniques to explain them to its workers and visitors.
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Adebayo J, Gilmer J, Goodfellow I, Kim B (2018) Local explanation methods for deep neural networks lack sensitivity to parameter values. In: International conference on learning representations workshop (ICLR)
Chakraborty S, Tomsett R, Raghavendra R, Harborne D, Alzantot M, Cerutti F, Srivastava M, Preece A, Julier S, Rao RM et al (2017) Interpretability of deep learning models: a survey of results. In: IEEE smart world congress 2017 workshop: DAIS
Doshi-Velez F, Kim B (2017) Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608
Evermann J, Rehse JR, Fettke P (2017) A deep learning approach for predicting process behaviour at runtime. In: Dumas M, Fantinato M (eds) Business process management workshops, Springer International Publishing, pp 327–338
Evermann J, Rehse JR, Fettke P (2017) Predicting process behaviour using deep learning. Decis Support Syst 100:129–140
Gedeon TD (1997) Data mining of inputs: analysing magnitude and functional measures. Int J Neural Syst 8(02):209–218
Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L (2018) Explaining explanations: an approach to evaluating interpretability of machine learning. arXiv preprint arXiv:1806.00069
Guidotti R, Monreale A, Turini F, Pedreschi D, Giannotti F (2018) A survey of methods for explaining black box models. arXiv preprint arXiv:1802.01933
Gunning D (2017) Explainable artificial intelligence (XAI). Tech. rep., Defense Advanced Research Projects Agency (DARPA)
Hall P, Kurka M, Bartz A (2017) Using H2O driverless ai. Tech. rep., H2O.ai, Inc. http://docs.h2o.ai/driverless-ai/latest-stable/docs/booklets/DriverlessAIBooklet.pdf. Accessed 1 Aug 2018
Halper F (2015) Operationalizing and embedding analytics for action. Tech. rep., TDWI Research
Lipton ZC (2018) The mythos of model interpretability. Queue 16(3):30
Mehdiyev N, Evermann J, Fettke P (2018) A novel business process prediction model using a deep learning method. Business Inf Syst Eng. https://doi.org/10.1007/s12599-018-0551-3 (online first)
Mehdiyev N, Lahann J, Emrich A, Enke D, Fettke P, Loos P (2017) Time series classification using deep learning for process planning: a case from the process industry. Proc Comput Sci 114:242–249
Miller T (2017) Explanation in artificial intelligence: insights from the social sciences. arXiv preprint arXiv:1706.07269
Montavon G, Samek W, Mller KR (2018) Methods for interpreting and understanding deep neural networks. Digit Signal Process 73:1–15
Nugent C, Cunningham P (2005) A case-based explanation system for black-box systems. Artif Intell Rev 24(2):163–178
Nunes I, Jannach D (2017) A systematic review and taxonomy of explanations in decision support and recommender systems. User Model User-Adapt Interact 27(3):393–444
Rehse JR, Dadashnia S, Fettke P (2018) Business process management for Industry 4.0 Three application cases in the DFKI-Smart-Lego-Factory. it Inf Technol 60(3):133–141
Ribeiro MT, Singh S, Guestrin C (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, ACM, pp 1135–1144
Sørmo F, Cassens J, Aamodt A (2005) Explanation in case-based reasoning-perspectives and goals. Artif Intell Rev 24(2):109–143
Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: methods and applications. J Manuf Syst 48:144–156
Wick MR, Thompson WB (1992) Reconstructive expert system explanation. Artif Intell 54(1):33–70
Zhong RY, Xu X, Klotz E, Newman ST (2017) Intelligent manufacturing in the context of industry 4.0: a review. Engineering 3(5):616–630
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We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.
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Rehse, JR., Mehdiyev, N. & Fettke, P. Towards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory. Künstl Intell 33, 181–187 (2019). https://doi.org/10.1007/s13218-019-00586-1
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DOI: https://doi.org/10.1007/s13218-019-00586-1