Kusumasari, Bevaola and Yahya, Bernardo Nugroho (2024) Algorithmic governance and AI: balancing innovation and oversight in Indonesian policy analyst. AI and Society. ISSN 09515666
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Abstract
The objective of this study is to examine the effects of generative artificial intelligence (AI) tools, with a specific focus on ChatGPT, on the analytical proficiencies of policy analysts operating in Indonesia. Considering the increasing intricacies of contemporary governance and the emergence of "wicked problems," this study investigates the potential of AI to facilitate the development of inventive, data-centric public policies. Involving postgraduate students in a quasi-experimental design, this study investigated the efficacy of ChatGPT in assisting in the development of policy solutions pertaining to air pollution in Jakarta. Specialized prompt engineering techniques are utilized in this study to enhance the utility of ChatGPT for policy analysis. The results suggest that, although generative AI tools such as ChatGPT provide systematic frameworks for tackling complex policy issues and may enhance analytical skills, they still require additional refinement to increase their empirical support and practical applicability in the Indonesian policy context. In accordance with incrementalism theory, the study concludes that incorporating AI into policy analysis can facilitate the gradual formulation of evidence-based policies, offering a pragmatic approach to the resolution of complex problems in the public sector.
Item Type: | Article |
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Uncontrolled Keywords: | ChatGPT; Generative artificial intelligence (AI); Indonesia; Policy analyst; Public policy wicked problems incrementalism; Quasi-experimental design |
Subjects: | H Social Sciences > H Social Sciences (General) J Political Science > JS Local government Municipal government |
Divisions: | Faculty of Political and Social Sciences > Public Policy and Management |
Depositing User: | Yuli Hesti Wahyuningsih |
Date Deposited: | 28 Oct 2024 07:31 |
Last Modified: | 28 Oct 2024 07:31 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/10005 |