Continuous Integration for Ethical, Collaborative Data Science
Creators: name: Whitaker, Kirstie
Publisher: name: Zenodo
Year: 2021

description: <em>Kirstie's slides for her talk at the University of Exeter Institute for Data Science and Artificial Intelligence (IDSAI) Data Science Week on 29 June 2021.</em> <strong>Title</strong>: Continuous Integration for Ethical, Collaborative Data Science <strong>Abstract</strong>: Data science is innovative, collaborative, and interdisciplinary research that sits in the overlap of various different venn diagrams. Most importantly, these projects must be delivered by teams of experts. The best work will bring together people with domain knowledge of both the data and the field's important research questions, those who understand the nuances and applications of statistical modelling techniques, research software and data engineers, and social scientists and people with lived experience who can inform the ethical considerations and impact of the project. In this talk, Kirstie will propose a "DevOps" approach to collaborative research ethics review. For the deployment of software at scale, DevOps means bringing together, in a tight feedback loop, expertise in software development and information technology operations. She will map the DevOps concept of "continuous integration" to research ethics review. This process must balance the legal and risk mitigation responsibilities of an organisation with that same organisation's ambition to take on the most challenging research questions to make a positive change in the world. By moving away from ethics review as a single moment near the start of a project, everyone can benefit from ongoing connections between members of a research team and external experts who can inform the implementation of their ideas. Kirstie will fit this work into the mission of <em>The Turing Way</em>, a community-created open source handbook for reproducible, ethical, inclusive and collaborative data science. Attendees will not leave the talk with all of the answers they need to complete a checkbox exercise for "ethical" research. Rather they will - hopefully - see a few more connections between the many dimensions of openness in research practice and how their participation in an inclusive, supportive, kind and inspiring research culture can improve the quality of data science outputs for all. <strong>Bio</strong>: Kirstie Whitaker leads the Tools, Practices and Systems Research Programme at The Alan Turing Institute (London, UK). The TPS community's mission is to invest in the people who sustain the open infrastructure ecosystem for data science. Kirstie is the lead developer of <em>The Turing Way</em>, an openly developed educational resource inspire, train and enable researchers and citizen scientists across government, industry, academia and third sector organisations to apply open source practices to their work. Kirstie is a passionate advocate for making science "open for all" by promoting equity and inclusion for people from diverse backgrounds, and by changing the academic incentive structure to reward collaborative working. She is the chair of the Turing Institute's Ethics Advisory Group, a Fulbright scholarship alumna and was a 2016/17 Mozilla Fellow for Science. Kirstie was named, with her collaborator Petra Vertes, as a 2016 Global Thinker by Foreign Policy magazine. You can follow her and her dog's adventures on Twitter @kirstie_j. <strong>Useful links:</strong> The book: https://the-turing-way.netlify.com Github repository: https://github.com/alan-turing-institute/the-turing-way Slack: https://tinyurl.com/jointuringwayslack Online Collaboration Cafe outline and schedule: https://github.com/alan-turing-institute/the-turing-way/blob/master/project_management/online-collaboration-cafe.md Twitter: https://twitter.com/turingway Newsletter: https://tinyletter.com/TuringWay
description: This work was supported by The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "Tools, Practices and Systems" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1.