AI Readiness for the SDGs

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Workshop 412

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[Read more session reports and updates from the 14th Internet Governance Forum]

The round-table discussion focused on artificial intelligence (AI) for the sustainable development goals (SDGs), possible harms of AI, how to measure SDG goals progress and use AI to achieve the SDGs. The session was moderated by Mr Alex Comninos (Senior Researcher, Research ICT Africa).

Yet another technology is taking the world by storm, but there are opportunities for it to do good. Relative to the SDGs, AI is an advanced capability which requires connectivity and capability at a technical level to understand data. Mr Greg Shannon (Chief Scientist, Software Engineering Institute, Carnegie Mellon University) emphasised the digital literacy skills needed in order to enable AI to have an impact. He referred to the open source community which has been a good place to understand and incorporate concepts of data privacy and protection to ensure fairness and inclusion. He gave an example of a farmer wanting to quickly identify the kind of insect attacking their crops, taking a picture of the plant leaf and being able to identify it.

Mr Dongi Lee (Committee Member, Korean Government Alliance) advocated for AI as a tool rather than a complete technology for the SDGs. Technology is rapidly developing, and AI policy now needs to be considered. He compared AI to magic, giving the example of creating images and videos in seconds and finding patterns including text and structure of unstructured data.

Regarding AI readiness for the SDGs and what it means from a development perspective for Africa, Mr Raymond Onuoha (Researcher, Research ICT Africa) mentioned that the AI era brings data revolution that is critical for promoting and achieving the SDGs and helping in measuring progress based on analysis of data. Having massive data is helpful for countries to plan, design, and implement the development of public policies. He noted that the software part of AI will require the redefinition of principles, norms, and policies for data governance in the digital era. From the hardware side, there is a need to restructure institutional configurations for sustainable development and governance.

Attendees talked about other issues like raw data that is collected directly from the source, giving an example of population census and how the Internet of Things (IoT) can be used to collect data on locations. It was noted that AI is largely misunderstood but the automation process requires pre-processing of raw data, otherwise the data collected will not be useful. It is important to understand the nuances and consider options because one size cannot fit all. Other questions and comments were raised about raising capital to fund innovations, capacity for policymakers to understand AI in order to develop relevant policy, the lack of consultation with intended end-users of technologies, user-centred design, responsibilities for different stakeholders like companies, government, and the technical community.

By Sarah Kiden

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