eprintid: 10206219
rev_number: 9
eprint_status: archive
userid: 699
dir: disk0/10/20/62/19
datestamp: 2025-03-18 13:22:51
lastmod: 2025-03-18 13:22:51
status_changed: 2025-03-18 13:22:51
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Anggunia, Sofiarti Dyah
creators_name: Sowell, Jesse
creators_name: Pérez-Ortiz, María
title: Decoding development: the AI frontier in policy crafting: A systematic review
ispublished: pub
divisions: UCL
divisions: B04
divisions: F48
divisions: J39
note: © The Author(s), 2025. Published by Cambridge University Press.This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
abstract: In today’s world, smart algorithms—artificial intelligence (AI) and other intelligent systems—are pivotal for promoting the development agenda. They offer novel support for decision-making across policy planning domains, such as analysing poverty alleviation funds and predicting mortality rates. To comprehensively assess their efficacy and implications in policy formulation, this paper conducts a systematic review of 207 publications. The analysis underscores their integration within and across stages of the policy planning cycle: problem diagnosis and goal articulation; resource and constraint identification; design of alternative solutions; outcome projection; and evaluation. However, disparities exist in smart algorithm applications across stages, economic development levels, and Sustainable Development Goals (SDGs). While these algorithms predominantly focus on resource identification (29%) and contribute significantly to designing alternatives—such as long-term national energy policies—and projecting outcomes, including predicting multi-scenario land-use ecological security strategies, their application in evaluation remains limited (10%). Additionally, low-income nations have yet to fully harness AI’s potential, while upper-middle-income countries effectively leverage it. Notably, smart algorithm applications for SDGs also exhibit unevenness, with more emphasis on SDG 11 than on SDG 5 and SDG 17. Our study identifies literature gaps. Firstly, despite theoretical shifts, a disparity persists between physical and socioeconomic/environmental planning applications. Secondly, there is limited attention to policy-making in development initiatives, which is critical for improving lives. Future research should prioritise developing adaptive planning systems using emerging powerful algorithms to address uncertainty and complex environments. Ensuring algorithmic transparency, human-centered approaches, and responsible AI are crucial for AI accountability, trust, and credibility.
date: 2025
date_type: published
publisher: Cambridge University Press (CUP)
official_url: https://doi.org/10.1017/dap.2025.10
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2369283
doi: 10.1017/dap.2025.10
lyricists_name: Sowell, Jesse
lyricists_name: Perez Ortiz, Maria
lyricists_name: Anggunia, Sofiarti
lyricists_id: JSOWE95
lyricists_id: MPERE90
lyricists_id: SDANG43
actors_name: Anggunia, Sofiarti
actors_id: SDANG43
actors_role: owner
full_text_status: public
publication: Data & Policy
volume: 7
article_number: e31
issn: 2632-3249
citation:        Anggunia, Sofiarti Dyah;    Sowell, Jesse;    Pérez-Ortiz, María;      (2025)    Decoding development: the AI frontier in policy crafting: A systematic review.                   Data & Policy , 7     , Article e31.  10.1017/dap.2025.10 <https://doi.org/10.1017/dap.2025.10>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10206219/1/Anggunia_decoding-development-the-ai-frontier-in-policy-crafting-a-systematic-review.pdf