Safe, ethical, and open research in the age of artificial intelligence

Abstract

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It has been 40 years since the launch of the GNU project and the Free Software Movement in September 1983. Open Source followed in 1998, Open Access in 2002, Citizen Science in 1995, the Bermuda Principles for open genomic data in 1996, and Open Science was first used as a phrase in 1985. Elinor Ostrom published her Nobel Prize winning "Governing the Commons" in 1990. There has been exceptional adoption and promotion of open research practices in the last 20 years, and there has also been significant drift from the original vision of democratising access to knowledge. In the age of artificial intelligence, are we meeting our ethical responsibilities to use data responsibly? Are we using scholarly communications to dismantle oppressive and exclusive power structures? What more work must we undertake? In this keynote presentation, Dr Kirstie Whitaker will identify core tenets of open research in 2023 and propose an integration with the SAFE-D principles for responsible research and innovation. Developed by David Leslie at The Alan Turing Institute in 2019, these principles facilitate reflection and self-assessment of the safety and sustainability, accountability, fairness and non-discrimination, and explainability and transparency of a research and innovation outputs, including a consideration of data quality, integrity, protection and privacy. Kirstie will explore with the audience whether the scholarly publishing community is ready to address the biggest societal challenges of our time, how we assess and incentivise responsible and ethical development of socio-technical solutions, and how our infrastructure can facilitate interdisciplinary teams to create outputs that are greater than the sum of their parts. All attendees will be invited to join the 800+ members of the open source, open collaboration, and community-driven Turing Way community. Developed on GitHub under open source licences, our shared goal is to provide all the information that researchers and data scientists in academia, industry and the public sector need to ensure that the projects they work on are easy to reproduce and reuse.