Responsible data science: using event data in a “people friendly” manner

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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.

    Original languageEnglish
    Title of host publicationEnterprise Information Systems
    Subtitle of host publication18th International Conference, ICEIS 2016, Rome, Italy, April 25–28, 2016, Revised Selected Papers
    EditorsS. Hammoudi, L.A. Maciaszek, M.M. Missikoff, O. Camp, J. Cordeiro
    Place of PublicationDordrecht
    PublisherSpringer
    Pages3-28
    Number of pages26
    ISBN (Print)978-3-319-62386-3
    DOIs
    Publication statusPublished - 2017
    Event18th International Conference on Enterprise Information Systems (ICEIS 2016) - Rome, Italy
    Duration: 25 Apr 201628 Apr 2016
    Conference number: 18
    http://www.iceis.org/?y=2016

    Publication series

    NameLecture Notes in Business Information Processing
    Volume291
    ISSN (Print)1865-1348

    Conference

    Conference18th International Conference on Enterprise Information Systems (ICEIS 2016)
    Abbreviated titleICEIS 2016
    CountryItaly
    CityRome
    Period25/04/1628/04/16
    Internet address

    Keywords

    • Accuracy
    • Big data
    • Confidentiality
    • Data science
    • Fairness
    • Process mining
    • Transparency

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