‘Solving for X?’ Towards a problem-finding framework that grounds long-term governance strategies for artificial intelligence

Research output: Contribution to journalJournal articlepeer-review

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‘Solving for X?’ Towards a problem-finding framework that grounds long-term governance strategies for artificial intelligence. / Liu, Hin-Yan; Maas, Matthijs Michiel.

In: Futures The journal of policy, planning and futures studies, Vol. 126, 102672, 2021.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Liu, H-Y & Maas, MM 2021, '‘Solving for X?’ Towards a problem-finding framework that grounds long-term governance strategies for artificial intelligence', Futures The journal of policy, planning and futures studies, vol. 126, 102672. https://doi.org/10.1016/j.futures.2020.102672

APA

Liu, H-Y., & Maas, M. M. (2021). ‘Solving for X?’ Towards a problem-finding framework that grounds long-term governance strategies for artificial intelligence. Futures The journal of policy, planning and futures studies, 126, [102672]. https://doi.org/10.1016/j.futures.2020.102672

Vancouver

Liu H-Y, Maas MM. ‘Solving for X?’ Towards a problem-finding framework that grounds long-term governance strategies for artificial intelligence. Futures The journal of policy, planning and futures studies. 2021;126. 102672. https://doi.org/10.1016/j.futures.2020.102672

Author

Liu, Hin-Yan ; Maas, Matthijs Michiel. / ‘Solving for X?’ Towards a problem-finding framework that grounds long-term governance strategies for artificial intelligence. In: Futures The journal of policy, planning and futures studies. 2021 ; Vol. 126.

Bibtex

@article{18804e53500d4ba09780f80dfa25fec5,
title = "{\textquoteleft}Solving for X?{\textquoteright} Towards a problem-finding framework that grounds long-term governance strategies for artificial intelligence",
abstract = "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{\textquoteright}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 {\textquoteleft}problem-solving{\textquoteright} approaches may be necessary, but are also insufficient in the face of many of the {\textquoteleft}wicked problems{\textquoteright} 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 {\textquoteleft}problem-finding{\textquoteright} orientation. We argue that creative, {\textquoteleft}problem-finding{\textquoteright} 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 {\textquoteleft}levels{\textquoteright} of governance: problem-solving research generally frames new issues as (0) {\textquoteleft}Business As Usual{\textquoteright} or as (1) {\textquoteleft}Governance Puzzle{\textquoteright}. In contrast, problem-finding approaches examine (2) {\textquoteleft}Governance Disruptors{\textquoteright} and (3) {\textquoteleft}Macrostrategic Trajectories{\textquoteright}. 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. ",
author = "Hin-Yan Liu and Maas, {Matthijs Michiel}",
year = "2021",
doi = "10.1016/j.futures.2020.102672",
language = "English",
volume = "126",
journal = "Futures",
issn = "0016-3287",
publisher = "Pergamon Press",

}

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