Lessons learned from developing a COVID-19 algorithm governance framework in Aotearoa New Zealand. Issue 1 (1st January 2023)

• Record Type: Journal Article
• Authors: Wilson, Daniel; Tweedie, Frith; Rumball-Smith, Juliet; Ross, Kevin; Kazemi, Alex; Galvin, Vince; Dobbie, Gillian; Dare, Tim; Brown, Pieta; Blakey, Judy
• Is Part Of: Journal of the Royal Society of New Zealand. Volume 53:Issue 1(2023)
• Page Start: 82
• Page End: 94
• Publication Date: 2023-01-01
• Subjects: Algorithms -- artificial intelligence -- COVID-19 -- clinical decision support -- governance framework -- healthcare data or healthcare algorithms -- prediction models -- risk models; Science -- Periodicals; 505
• DOI: 10.1080/03036758.2022.2121290
• Languages: English
• ISSNs: 0303-6758

Abstract

ABSTRACT: Aotearoa New Zealand's response to the COVID-19 pandemic has included the use of algorithms that could aid decision making. Te Pokapū Hātepe o Aotearoa, the New Zealand Algorithm Hub, was established to evaluate and host COVID-19 related models and algorithms, and provide a central and secure infrastructure to support the country's pandemic response. A critical aspect of the Hub was the formation of an appropriate governance group to ensure that algorithms being deployed underwent cross-disciplinary scrutiny prior to being made available for quick and safe implementation. This framework necessarily canvassed a broad range of perspectives, including from data science, clinical, Māori, consumer, ethical, public health, privacy, legal and governmental perspectives. To our knowledge, this is the first implementation of national algorithm governance of this type, building upon broad local and global discussion of guidelines in recent years. This paper describes the experiences and lessons learned through this process from the perspective of governance group members, emphasising the role of robust governance processes in building a high-trust platform that enables rapid translation of algorithms from research to practice.