- Felix Mannhardt, Eindhoven University of Technology
- Flavia Maria Santoro, Universidade do Estado do Rio de Janeiro
- Majid Rafiei, RWTH Aachen Univerity
- Stephan Fahrenkrog-Petersen, Humboldt-Universität zu Berlin
Process mining has been successfully applied in analysing and improving processes based on event logs in all kinds of environments. Responsible process mining is highly relevant to our more and more data-driven society and has received less focus. FACT (Fair, Accurate, Confidential, and Transparent) and similar other principles for data science and machine learning have been proposed (cf. https://redasci.org/) to guide the development and application of data science. Issues such as lacking data quality in event logs, identifiable personal data in event logs, biased event logs, learning, discovery techniques with opaque parameters, uncertain event data and many more aspects threaten the compliance to these principles in process analytics. However, process mining could also be applied to help with the “FACT-ful” application of machine learning and other data-driven techniques by bringing transparency. All aspects of Responsible Process Mining (RPM) are in the scope of this workshop covering a wide range of concepts and challenges such as fairness, accuracy, confidentiality, privacy, transparency, explainability, trust, data quality, ethics, security, etc.