AI is enhancing agricultural productivity, enabling precision farming, and optimizing food distribution through data-driven insights. In this chart we see the prevalence of moderate or severe food insecurity in the population (%) taking into account the news on AI affecting SDG 2..

This perspective offers a real-time view of the worldwide news on hunger-related topics, providing us with a media perspective on the progress at the different fronts of SDG 2.

 

Developed in collaboration with 

A SDG Live Report is what it is all about! Here, you can have a real-time perspective of the state of the world in regard to AI and sustainability, tracking some of the main issues to address in the near future.

 

AI has significantly impacted the progress of Sustainable Development Goal 2 (SDG 2), which aims to end hunger, achieve food security and improved nutrition, and promote sustainable agriculture. Between 2015 and 2023, scientific publications on the impact of AI in SDG 2 numbered 265,272, indicating robust research activity and innovation. These studies span various applications, including precision agriculture, crop monitoring, pest control, and yield prediction. The vast body of research highlights AI’s potential to optimize resource use, enhance productivity, and reduce food waste. Media exposure, with 10,532 news articles, underscores growing public awareness and interest in the role of AI in transforming agricultural practices. Policymakers have also recognized AI’s importance, as evidenced by the development of 2,681 AI policies aimed at advancing SDG 2. These policies facilitate the integration of AI technologies in agricultural sectors, promoting sustainable and efficient practices.

Looking ahead, the continued growth in scientific research, media coverage, and policy development suggests a promising trajectory for AI’s contribution to SDG 2 over the next 5 to 10 years. We can anticipate accelerated innovation in AI-driven agricultural technologies, leading to more widespread adoption of precision farming, automated irrigation systems, and predictive analytics. As AI becomes more integrated into agricultural practices, smallholder farmers will benefit from improved crop management and increased resilience to climate change. Additionally, the ongoing development of supportive policies will likely enhance collaboration between governments, research institutions, and the private sector, fostering an environment conducive to sustainable agricultural advancements. Overall, AI’s role in achieving SDG 2 will continue to expand, driving progress towards a more food-secure and sustainable future.

 

Developed in collaboration with the European Commission project 

Here is your window to global efforts towards food security. This dashboard is configurable and can be integrated into other systems to bring actionable technology into the systems of research institutions, governments and enterprises that are willing to make a difference.

 

 

Developed in collaboration with the European Commission project 

Avoiding data bias in AI systems is crucial to ensure fair, accurate, and equitable outcomes, preventing the reinforcement of existing inequalities and enabling more trustworthy and inclusive technologies. Here you will see a dashboard analysing the bias related to the data ingested in this observatory for SDG 2.

For analysis we use OECD AI Policy documents. Some of those documents are very large, and we split each document into smaller parts (so called “chunks”), which can contain multiple paragraphs. The reason for this is to prepare data for easier analysis with large language models, so called Retrieval-Augmented Generation (RAG). RAG is an advanced technique that combines retrieval-based methods with generative models to improve the performance of tasks such as question answering, text generation, and other natural language processing (NLP) applications. For each chunk then the sentiment is computed based on VADER (Valence Aware Dictionary and sEntiment Reasoner) methodology. Since VADER is known to have weak multilingual capabilities, all the documents were machine translated into English first.

While the results of this procedure are reliant not only upon the accuracy of the sentiment analysis tool, but also upon the accuracy of machine translation, it is important to stress that sentiment analysis is less sensitive to common machine translation problems than other usages, because sentiment analysis usually focuses on identifying the polarity (positive, negative, neutral) of a text rather than understanding its full semantic content. Also, sentiments in text are often expressed redundantly, which can help mitigate the impact of translation errors. As a result, minor translation errors that do not alter the overall sentiment and do not significantly impact the sentiment analysis is possible.

For the purpose of this analysis, they computed the average sentiment of (chunks of) AI policy documents for each country. We are presenting the visualisation of average sentiment of countries’ AI policy documents on the map. Since AI policy documents are mostly documents of legal nature (acts, policies, regulatory and governance frameworks), the sentiment should be mostly neutral, however, the analysis shows that there are country differences.

VADER computes positive, negative and neutral sentiment. Each of those values are between 0 and 1. The score indicates the proportion of text that is considered positive, negative and neutral. The sum of negative, positive, and neutral sentiment scores always equals 1, however in practice the sum of three sentiment scores can sometimes slightly exceed or fall below 1 due to floating-point precision errors or rounding issues that occur during computation.

Developed in collaboration with the European Commission project