Steering open-source AI to accelerate the sustainable development goals

Source Domain: www.nature.com

Lifecycle Management of Open-source AI: Integrating sustainability across the entire AI development lifecycle—from computing hardware to model development and application deployment—requires strategic actions emphasizing energy efficiency, reusable AI architectures, and deployment management to mitigate waste.
Quantitative AI Impact Indicators: Establishing a framework for standardized quantitative indicators to measure the impact of open-source AI on the Sustainable Development Goals (SDGs) is essential. This includes creating composite frameworks for multiple impact indicators and adjusting local indicators to fit regional social, economic, and environmental characteristics.
Trustworthy Open-source AI: Ensuring the trustworthiness of open-source AI entails using security-audited training datasets, implementing explainable AI technologies, and establishing regulatory policies. It requires cooperative accountability among developers, users, and governments to prevent the misuse of AI and promote ethical application.
Global Knowledge-Sharing Mechanisms and Regional Cooperation: Building open-access platforms that ensure equitable access to computation, data, and foundational AI models is critical. The United Nations and international institutions like the Global Dialog on AI Governance can play roles in governing these infrastructures, reducing inequity, and fostering global and regional partnerships to overcome data privacy and sovereignty issues.

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