8 January 2021

Liu & Maas publish new ‘problem-finding’ AI governance model in Futures

‘Solving for X?’ charts 4 levels of artificial intelligence regulation, with own strengths & limits into long-term

Dr. Hin-Yan Liu (CECS) and Matthijs Maas (CSER, University of Cambridge), scholars at the research group AI-LeD, have co-authored ‘‘Solving for X?’ Towards a problem-finding framework to ground long-term governance strategies for artificial intelligence’ in the journal Futures, for a special issue collection on ‘long-term governance’.

In their paper, Liu & Maas note that many current governance approaches to artificial intelligence (AI) focus on clear, discrete clusters of pre-defined policy problems (e.g. ‘self-driving cars’; ‘drones’; ‘deepfakes’). They argue however that such ‘problem-solving’ approaches may prove insufficient at organizing adequate AI governance into the long-term.

Accordingly, they develop a complementary ‘problem-finding’ framework for articulating long-term governance strategies to AI (and other technologies). They argue that such creative research is not only warranted scientifically, but will also be critical in the formulation of governance strategies that are effective, meaningful, and resilient over the long-term.

Picture in full size

Their framework distinguishes between four distinct ‘levels’ of governance to AI. problem-solving research generally approaches AI (governance) issues from a perspective of (Level 0) ‘business-as-usual’ or as (Level 1) ‘governance puzzle-solving’. In contrast, problem-finding approaches emphasize (Level 2) ‘governance Disruptor-Finding’; or (Level 3) ‘Charting Macrostrategic Trajectories’. Finally, they explore the additional nuances and implications of this model: when and why do governance strategies fail by focusing too much on a single level? How do these four levels chart the boundary conditions of ‘governability’ - that is, create a ‘Governance Goldilocks Zone’? This holistic framework, it is suggested, will be key at promoting dialogue amongst scholars, amongst AI policy domains, and between theory and practice, in order to help society shape its trajectory around its relation with AI technology not just in the near-, but also in the long-term future.

Citation Information

Liu, Hin-Yan, and Matthijs M. Maas. “‘Solving for X?’ Towards a Problem-Finding Framework to Ground Long-Term Governance Strategies for Artificial Intelligence.” Futures 126 (February 1, 2021): 22. https://doi.org/10.1016/j.futures.2020.102672.

See also: