research-article

Rethinking AI Strategy and Policy as Entangled Super Wicked Problems

Author:

Ross GruetzemacherAuthors Info & Claims

AIES '18: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

Page 122

https://doi.org/10.1145/3278721.3278746

Published: 27 December 2018 Publication History

Abstract

This paper attempts a preliminary analysis of the general approach to AI strategy/policy research through the lens of wicked problems literature. Wicked problems are a class of social policy problems for which traditional methods of resolution fail. Super wicked problems refer to even more complex social policy problems, e.g. climate change. We first propose a hierarchy of three classes of AI strategy/policy problems, all wicked or super wicked problems. We next identify three independent super wicked problems in AI strategy/policy and propose that the most significant of these challenges - the development of safe and beneficial artificial general intelligence - to be significantly more complex and nuanced, thus posing a new degree of 'wickedness.' We then explore analysis and techniques for addressing wicked problems and super wicked problems. This leads to a discussion of the implications of these ideas on the problems of AI strategy/policy.

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Cited By

Wiesmüller SWiesmüller S(2023)Introduction and Theoretical FoundationsThe Relational Governance of Artificial Intelligence10.1007/978-3-031-25023-1_1(1-23)Online publication date: 4-Feb-2023
https://doi.org/10.1007/978-3-031-25023-1_1
Carayannis EDraper J(2022)Optimising peace through a Universal Global Peace Treaty to constrain the risk of war from a militarised artificial superintelligenceAI & SOCIETY10.1007/s00146-021-01382-y38:6(2679-2692)Online publication date: 11-Jan-2022
https://doi.org/10.1007/s00146-021-01382-y
Vashishtha SRhinard M(2021)Big Data as a Creeping CrisisUnderstanding the Creeping Crisis10.1007/978-3-030-70692-0_5(69-86)Online publication date: 11-May-2021
https://doi.org/10.1007/978-3-030-70692-0_5
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cover image ACM Conferences

AIES '18: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society

December 2018

406 pages

ISBN:9781450360128

DOI:10.1145/3278721

Program Chairs:
Jason Furman
Harvard University, USA
,
Gary Marchant
Arizona State University, USA
,
Huw Price
Cambridge University, UK
,
Francesca Rossi
IBM Research, USA & University of Padova, Italy

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGAI: ACM Special Interest Group on Artificial Intelligence

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Association for Computing Machinery

New York, NY, United States

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Published: 27 December 2018

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AIES '18: AAAI/ACM Conference on AI, Ethics, and Society

February 2 - 3, 2018

LA, New Orleans, USA

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AIES '18 Paper Acceptance Rate 61 of 162 submissions, 38%;

Overall Acceptance Rate 61 of 162 submissions, 38%

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Cited By

Wiesmüller SWiesmüller S(2023)Introduction and Theoretical FoundationsThe Relational Governance of Artificial Intelligence10.1007/978-3-031-25023-1_1(1-23)Online publication date: 4-Feb-2023
https://doi.org/10.1007/978-3-031-25023-1_1
Carayannis EDraper J(2022)Optimising peace through a Universal Global Peace Treaty to constrain the risk of war from a militarised artificial superintelligenceAI & SOCIETY10.1007/s00146-021-01382-y38:6(2679-2692)Online publication date: 11-Jan-2022
https://doi.org/10.1007/s00146-021-01382-y
Vashishtha SRhinard M(2021)Big Data as a Creeping CrisisUnderstanding the Creeping Crisis10.1007/978-3-030-70692-0_5(69-86)Online publication date: 11-May-2021
https://doi.org/10.1007/978-3-030-70692-0_5
Gruetzemacher R(2019)A Holistic Framework for Forecasting Transformative AIBig Data and Cognitive Computing10.3390/bdcc30300353:3(35)Online publication date: 26-Jun-2019
https://doi.org/10.3390/bdcc3030035
Liu HMaas M(undefined)'Solving for X?' Towards a Problem-Finding Framework to Ground Long-Term Governance Strategies for Artificial IntelligenceSSRN Electronic Journal10.2139/ssrn.3761623
https://doi.org/10.2139/ssrn.3761623

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