Jakarta, INTI - The rapid development of artificial intelligence (AI) is driving an increasing need for AI data centers as the primary infrastructure for data processing and storage. Beyond its role in supporting digital transformation, the development of AI data centers also presents social, environmental, and governance implications that need to be addressed through appropriate policies.
Yanuar Farida Wismayanti, Head of Research Center for Public Policy at BRIN, assessed that public attention has so far focused on the use of AI in various sectors.
"Currently, we are thinking a lot about how to use AI in various sectors. However, we also need to consider data security, data infrastructure, and the crucial importance of data centers. Besides driving economic growth, the development of AI data centers also has social and environmental impacts that require attention," Yanuar stated at the ELABORASI Special #20 event, held in Jakarta on Wednesday, June 3.
According to Yanuar, discussions on the governance and impact of AI data centers are becoming increasingly relevant as AI utilization increases in various fields. These discussions are crucial for enriching the perspectives of researchers and policymakers in formulating sustainable development strategies.
AI Development’s Accountability and Policy Challenges
During the event, Cahyo Trianggoro, a researcher at the Research Center for Public Policy, explained that the development of AI presents new challenges related to accountability. This occurs primarily because responsibility for AI use is distributed among technology developers, service providers, and various parties in the digital supply chain. He also highlighted the potential for access disparities resulting from the concentration of generative AI development in organizations with significant resources.
According to Cahyo, attention also needs to be paid to ethical aspects of the AI technology supply chain, including the use of natural resources and labor conditions associated with the production of semiconductors and high-performance computing devices. Environmentally, AI data center operations require intensive energy and cooling systems, which have implications for increased resource use, environmental emissions, and pressure on water availability.
Therefore, he encouraged the implementation of responsible AI governance through increased transparency in data use, the use of more efficient AI models, the application of FAIR (Findable, Accessible, Interoperable, Reusable) principles, and strengthened labor protections. He also emphasized the importance of social and environmental impact analysis for large-scale AI projects.
Meanwhile, Kris Hartley, an assistant professor at Arizona State University, explained that AI data centers are a physical manifestation of AI development. This infrastructure presents public policy challenges related to energy needs, water use, spatial planning, and public acceptance of data center construction.
Hartley cited the example of how several communities in the United States are beginning to question the impact of data centers on the environment and the availability of local resources. He argued that AI data center development needs to be understood as part of an interconnected socio-ecological-technological system. Thus, development decisions should not only be oriented toward investment and economic growth but also consider environmental sustainability, government governance capacity, and the interests of affected communities.
Conclusion
The Head of Research Center for Public Policy at BRIN, Yanuar Farida Wismayanti, emphasized the importance of paying attention to data security, digital infrastructure, and data centers as the foundation for AI utilization. BRIN researcher, Cahyo Trianggoro, warned of accountability challenges, potential gaps in technology access, supply chain ethics issues, and high energy and water demands that could increase environmental emissions. He encouraged responsible AI governance through data transparency, the use of efficient AI models, the application of FAIR principles, labor protection, and socio-environmental impact analysis for large-scale AI projects.
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