Challenges and best practices in corporate AI governance: Lessons from the biopharmaceutical industry

Abstract

While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management.

The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks

“…In the literature, there is ample evidence of what constitutes biosafety and biosafety governance best practice ( Perkins et al, 2019 ; Wang and Zhang, 2019 ; Li et al, 2021 ; Mökander et al, 2022 ; Sandbrink, 2023b ) and the emphasis is on a mix of specific training and, relatedly, developing a safety and responsibility work culture. In previous decades, the few advanced labs that existed were “compliant” biocontainment actors, for which acceptable systems were in place.…”

“…Newer tools, such as machine learning-based topic models, enable spotting trends across a wide set of biosafety research publications ( Guan et al, 2022 ). AI-synbio governance ( Achim and Zhang, 2022 ; Mökander et al, 2022 ; Grinbaum and Adomaitis, 2023 ; Holland et al, 2024 ) is expected to be more of the above, but also requires AI skills and perspectives that go far beyond wet lab practices and will require updates to biosafety laws, regulation, governance, standardization ( Pei et al, 2022 ). It will change the role of the state ( Djeffal et al, 2022 ) as it will no longer be the primary norm setter or enforcer of responsibility.…”

“…At any level, the process is quite complex. For example, the corporate AI governance at British biopharma AstraZeneca includes compliance documents, a responsible AI playbook and consultancy service, an AI resolution board, and AI audits–emphasizing procedural regularity and transparency–and interlinking with existing procedures, structures, tools, and methods ( Mökander et al, 2022 ).…”

The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems

“…This is because implementing corporate AI governance in practice requires the active participation of a wide range of staff across an organisation, including people who lack technical training and skills(Mökander et al, 2022a). 9 According toWeber (1904), an ideal type is formed by the one-sided accentuation of one or more points of view, according to which concrete individual phenomena are arranged into a unified analytical construct.…”

Investigating accountability for Artificial Intelligence through risk governance: A workshop-based exploratory study

“…For example, checklists (e.g., AI HLEG, 2020; Algorithm Watch, 2021b ), impact assessments (e.g., Ada Lovelace Institute, AI Now, and Open Government Partnership, 2021 ); or technical tools (e.g., Vakkuri et al, 2021 ; Felländer et al, 2022 ) are often proposed. They are encouraged to “build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management” ( Mökander et al, 2022 ).…”