Artificial intelligence for social innovation: beyond the noise of algorithms and datafication

Calzada, Igor ORCID: https://orcid.org/0000-0002-4269-830X 2024. Artificial intelligence for social innovation: beyond the noise of algorithms and datafication. Sustainability 16 (19) , 8638. 10.3390/su16198638

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Abstract

In an era of rapid technological advancement, decisions about the ownership and governance of emerging technologies like Artificial Intelligence will shape the future of both urban and rural environments in the Global North and South. This article explores how AI can move beyond the noise of algorithms by adopting a technological humanistic approach to enable Social Innovation, focusing on global inequalities and digital justice. Using a fieldwork Action Research methodology, based on the Smart Rural Communities project in Colombia and Mozambique, the study develops a framework for integrating AI with SI. Drawing on insights from the AI4SI International Summer School held in Donostia-San Sebastián in 2024, the article examines the role of decentralized Web3 technologies—such as Blockchain, Decentralized Autonomous Organizations, and Data Cooperatives—in enhancing data sovereignty and fostering inclusive and participatory governance. The results demonstrate how decentralization can empower marginalized communities in the Global South by promoting digital justice and addressing the imbalance of power in digital ecosystems. The conclusion emphasizes the potential for AI and decentralized technologies to bridge the digital divide, offering practical recommendations for scaling these innovations to support equitable, community-driven governance and address systemic inequalities across the Global North and South.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Social Sciences (Includes Criminology and Education)
Publisher:	MDPI
ISSN:	2071-1050
Date of First Compliant Deposit:	31 October 2024
Date of Acceptance:	4 October 2024
Last Modified:	05 Dec 2024 12:22
URI:	https://orca.cardiff.ac.uk/id/eprint/173550

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