1. ACM U.S. Public Policy Council (USACM) and ACM Europe Council Policy Committee (EUACM): Statement on algorithmic transparency and accountability, May 2017. https://www.acm.org/binaries/content/assets/public-policy/2017_joint_statement_algorithms.pdf. Accessed 24 Jan 2019
2. Amershi, S., et al.: Software engineering for machine learning: a case study. In: 2019 IEEE/ACM 41st Int’l Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), pp. 291–300. IEEE (2019)
3. Cuevas-Vicenttín, V., et al.: Provone: a prov extension data model for scientific workflow provenance (2015). https://purl.dataone.org/provone-v1-dev
4. Curcin, V., Fairweather, E., Danger, R., Corrigan, D.: Templates as a method for implementing data provenance in decision support systems. J. Biomed. Inform. 65, 1–21 (2017)
5. Department of Health & Social Care (UK): Code of conduct for data-driven health and care technology, July 2019. https://www.gov.uk/government/publications/code-of-conduct-for-data-driven-health-and-care-technology/initial-code-of-conduct-for-data-driven-health-and-care-technology