‘Solving for X?’ Towards a problem-finding framework that grounds long-term governance strategies for artificial intelligence
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Standard
‘Solving for X?’ Towards a problem-finding framework that grounds long-term governance strategies for artificial intelligence. / Liu, Hin-Yan; Maas, Matthijs Michiel.
I: Futures The journal of policy, planning and futures studies, Bind 126, 102672, 2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - ‘Solving for X?’ Towards a problem-finding framework that grounds long-term governance strategies for artificial intelligence
AU - Liu, Hin-Yan
AU - Maas, Matthijs Michiel
PY - 2021
Y1 - 2021
N2 - Change is hardly a new feature in human affairs. Yet something has begun to change in change. In the face of a range of emerging, complex, and interconnected global challenges, society’s collective governance efforts may need to be put on a different footing. Many of these challenges derive from emerging technological developments – take Artificial Intelligence (AI), the focus of much contemporary governance scholarship and efforts. AI governance strategies have predominantly oriented themselves towards clear, discrete clusters of pre-defined problems. We argue that such ‘problem-solving’ approaches may be necessary, but are also insufficient in the face of many of the ‘wicked problems’ created or driven by AI. Accordingly, we propose in this paper a complementary framework for grounding long-term governance strategies for complex emerging issues such as AI into a ‘problem-finding’ orientation. We argue that creative, ‘problem-finding’ research is not only warranted scientifically, but also will be critical in the formulation of governance strategies that are effective, meaningful, and resilient over the long-term. We illustrate the relation and complementarity of problem-solving and problem-finding research through a framework that distinguishes between four distinct ‘levels’ of governance: problem-solving research generally frames new issues as (0) ‘Business As Usual’ or as (1) ‘Governance Puzzle’. In contrast, problem-finding approaches examine (2) ‘Governance Disruptors’ and (3) ‘Macrostrategic Trajectories’. Throughout our analysis, we apply and validate this theoretical framework to contemporary governance debates around AI. We conclude with observations on between-level complementarities and within-level path dependencies. We suggest that this framework can help underpins more holistic approaches for long-term strategy-making across diverse policy domains and contexts, and help cross the bridge between concrete policies on local solutions, and longer-term considerations of path-dependent societal trajectories to avert, or joint visions towards which global communities can or should be rallied.
AB - Change is hardly a new feature in human affairs. Yet something has begun to change in change. In the face of a range of emerging, complex, and interconnected global challenges, society’s collective governance efforts may need to be put on a different footing. Many of these challenges derive from emerging technological developments – take Artificial Intelligence (AI), the focus of much contemporary governance scholarship and efforts. AI governance strategies have predominantly oriented themselves towards clear, discrete clusters of pre-defined problems. We argue that such ‘problem-solving’ approaches may be necessary, but are also insufficient in the face of many of the ‘wicked problems’ created or driven by AI. Accordingly, we propose in this paper a complementary framework for grounding long-term governance strategies for complex emerging issues such as AI into a ‘problem-finding’ orientation. We argue that creative, ‘problem-finding’ research is not only warranted scientifically, but also will be critical in the formulation of governance strategies that are effective, meaningful, and resilient over the long-term. We illustrate the relation and complementarity of problem-solving and problem-finding research through a framework that distinguishes between four distinct ‘levels’ of governance: problem-solving research generally frames new issues as (0) ‘Business As Usual’ or as (1) ‘Governance Puzzle’. In contrast, problem-finding approaches examine (2) ‘Governance Disruptors’ and (3) ‘Macrostrategic Trajectories’. Throughout our analysis, we apply and validate this theoretical framework to contemporary governance debates around AI. We conclude with observations on between-level complementarities and within-level path dependencies. We suggest that this framework can help underpins more holistic approaches for long-term strategy-making across diverse policy domains and contexts, and help cross the bridge between concrete policies on local solutions, and longer-term considerations of path-dependent societal trajectories to avert, or joint visions towards which global communities can or should be rallied.
U2 - 10.1016/j.futures.2020.102672
DO - 10.1016/j.futures.2020.102672
M3 - Journal article
VL - 126
JO - Futures
JF - Futures
SN - 0016-3287
M1 - 102672
ER -
ID: 243910658