Page 42 - AI for Good - Impact Report

AI for Good

Sustainable Development Goal 2: No Hunger

End hunger, achieve food security and improved
nutrition and promote sustainable agriculture
As of 2024, the progress of SDG 2 is unbalanced. While 1
goal out of 8 (2.b Agricultural export subsidies) is advanc-
ing, the remaining seven goals are either regressing or
not being measured. This lack of progress means an
164
estimated 600 million people are projected to experience
hunger by 2030, with one in three individuals currently
facing moderate or severe food insecurity. Globally,
165
rising food prices, attributed to supply chain distribution
and conflict, are exacerbating the challenge for communi-
ties to meet their nutritional requirements. 166
AI and SDG 2 Impact
According to a study on the impact of AI on SDG
2 could act as an (positive) enabler for 75% of the
AI offers numerous use cases to advance SDG 2, such as precision targets and act as an inhibitor (negative) for 25% of
farming to optimize the use of resources (fertilizer or pesticides),- the targets. (Nature Communications, 2020)
167 monitoring environmental conditions such as air, soil and water Use case 1
quality to enhance crop resilience, 168 and tracking animals for their Using AI to drive new farming practices, such as
well-being. 169 These use cases can improve farming practices, precision farming, to reduce the quantity of pesti-
cides used and to drive food production.
reducing environmental impact while maximizing productivity.
In 2022, 10.5 billion tons of food waste were generated. 170 AI can
address food waste by helping individuals monitor consumption
and repurpose leftovers, as well as optimize the supply chain to

reduce waste and ensure food reaches those in need. 171 172 173 The
significance of AI for SDG 2 is evident in the substantial number of
relevant use cases across different UN repositories: 8 use cases out
of 40 in AI for Good: Innovate for Impact, 174 and approximately 60
use cases out of 408 in the UN Activities on AI. 175
link
However, the use of robots and other AI technology can be costly,
potentially limiting access to a minority of farmers and exacer- Use case 2
bating inequalities. 176 177 This could place additional pressure on Improving AI instruments to improve the efficiency

of farming practices and increase the quantity of
farmers with limited resources across various regions to compete food produced.
against these new technologies. Additionally, more efficient crops
do not necessarily guarantee environmental or social improve-
ments. 178 Focusing only on improved crop quality might disregard
the environmental impact of increased yields and production. The
impact of this could be better assessed due to improved monitor-
ing capabilities. Increased crop yields should not be at the expense
of reduced nutritional value for the end consumers. 179
Key Considerations for Stakeholders link
Use case 3
• Impact assessment: The development of AI use cases and Optimizing supply chain and food transport to
incentives should be aligned with OECD AI principles to minimize waste creation and maximize access to
maximize sustainable value creation. 180 The objective is to nutritional products for various regions.
prioritize governmental tools for AI use cases related to the
SDGs.
• Ownership sharing: To reduce the risk of monopoly on tech-
nologies, new business models should be considered, 181
where value is shared differently to minimize the increase in
inequalities and reward all contributors.

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