Holistic Explainability Requirements for End-to-End Machine Learning in IoT Cloud Systems
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A4 Artikkeli konferenssijulkaisussa
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Date
2021-09
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en
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7
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Proceedings - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021, pp. 188-194, Proceedings of the IEEE International Conference on Requirements Engineering
Abstract
End-to-end machine learning (ML) in Internet of Things (IoT) Cloud systems consists of multiple processes, covering data, model, and service engineering, and involves multiple stakeholders. Therefore, to be able to explain ML to relevant stakeholders, it is important to identify explainability requirements in a holistic manner. In this paper, we present our methodology to address explainability requirements for end-to-end ML in developing ML services to be deployed within IoT Cloud systems. We identify and classify explainability requirements engineering through (i) involvement of relevant stakeholders, (ii) end-to-end data, model, and service engineering processes, and (iii) multiple explainability aspects. We present our work with a case study of predictive maintenance for Base Transceiver Stations (BTS) in the telco domain.Description
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Nguyen, M-L, Phung, T, Ly, D-H & Truong, L 2021, Holistic Explainability Requirements for End-to-End Machine Learning in IoT Cloud Systems . in T Yue & M Mirakhorli (eds), Proceedings - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021 . Proceedings of the IEEE International Conference on Requirements Engineering, IEEE, pp. 188-194, International Workshop on Requirements Engineering for Explainable Systems, South Bend, Indiana, United States, 20/09/2021 . https://doi.org/10.1109/REW53955.2021.00034