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Responsible Data Science: Using Event Data in a “People Friendly” Manner

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Enterprise Information Systems (ICEIS 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 291))

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Abstract

The omnipresence of event data and powerful process mining techniques make it possible to quickly learn process models describing what people and organizations really do. Recent breakthroughs in process mining resulted in powerful techniques to discover the real processes, to detect deviations from normative process models, and to analyze bottlenecks and waste. Process mining and other data science techniques can be used to improve processes within any organization. However, there are also great concerns about the use of data for such purposes. Increasingly, customers, patients, and other stakeholders worry about “irresponsible” forms of data science. Automated data decisions may be unfair or non-transparent. Confidential data may be shared unintentionally or abused by third parties. Each step in the “data science pipeline” (from raw data to decisions) may create inaccuracies, e.g., if the data used to learn a model reflects existing social biases, the algorithm is likely to incorporate these biases. These concerns could lead to resistance against the large-scale use of data and make it impossible to reap the benefits of process mining and other data science approaches. This paper discusses Responsible Process Mining (RPM) as a new challenge in the broader field of Responsible Data Science (RDS). Rather than avoiding the use of (event) data altogether, we strongly believe that techniques, infrastructures and approaches can be made responsible by design. Not addressing the challenges related to RPM/RDS may lead to a society where (event) data are misused or analysis results are deeply mistrusted.

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Acknowledgements

This work is partly based by discussions in the context of the Responsible Data Science (RDS) collaboration involving principal scientists from Eindhoven University of Technology, Leiden University, University of Amsterdam, Radboud University Nijmegen, Tilburg University, VU University, Amsterdam Medical Center, VU Medical Center, Leiden University Medical Center, Delft University of Technology, and CWI.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Eindhoven University of Technology, PO Box 513, 5600 MB, Eindhoven, The Netherlands

    Wil M. P. van der Aalst

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  1. Wil M. P. van der Aalst
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Correspondence to Wil M. P. van der Aalst .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Wroclaw University of Economics, Wroclaw, Poland

    Leszek A. Maciaszek

  3. ISTC-CNR, Institute of Sciences and Technologies of Cognition, Rome, Italy

    Michele M. Missikoff

  4. MODESTE/ESEO, Angers, France

    Olivier Camp

  5. EST, Instituto Politécnico de Setúbal (IPS), Setúbal, Portugal

    José Cordeiro

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van der Aalst, W.M.P. (2017). Responsible Data Science: Using Event Data in a “People Friendly” Manner. In: Hammoudi, S., Maciaszek, L., Missikoff, M., Camp, O., Cordeiro, J. (eds) Enterprise Information Systems. ICEIS 2016. Lecture Notes in Business Information Processing, vol 291. Springer, Cham. https://doi.org/10.1007/978-3-319-62386-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-62386-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62385-6

  • Online ISBN: 978-3-319-62386-3

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