TY - CHAP A1 - Scherzinger, Stefanie A1 - Seifert, Christin A1 - Wiese, Lena T1 - The Best of Both Worlds: Challenges in Linking Provenance and Explainability in Distributed Machine Learning T2 - 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), 7-10 July 2019, Dallas, TX, USA N2 - Machine learning experts prefer to think of their input as a single, homogeneous, and consistent data set. However, when analyzing large volumes of data, the entire data set may not be manageable on a single server, but must be stored on a distributed file system instead. Moreover, with the pressing demand to deliver explainable models, the experts may no longer focus on the machine learning algorithms in isolation, but must take into account the distributed nature of the data stored, as well as the impact of any data pre-processing steps upstream in their data analysis pipeline. In this paper, we make the point that even basic transformations during data preparation can impact the model learned, and that this is exacerbated in a distributed setting. We then sketch our vision of end-to-end explainability of the model learned, taking the pre-processing into account. In particular, we point out the potentials of linking the contributions of research on data provenance with the efforts on explainability in machine learning. In doing so, we highlight pitfalls we may experience in a distributed system on the way to generating more holistic explanations for our machine learning models. KW - Computational modeling KW - Data models KW - Decision trees KW - distributed computing KW - Distributed databases KW - explainable machine learning KW - Machine learning Y1 - 2019 U6 - https://doi.org/10.1109/ICDCS.2019.00161 SP - 1620 EP - 1629 PB - IEEE ER -