AI assists in biodiversity conservation, ecosystem monitoring, and sustainable land management through data-driven insights and predictive analytics. In this chart we see the countries with a hight number of threatened plant species over time.

This perspective offers a real-time view of the worldwide news on life above water-related topics, providing us with a media perspective on the progress at the different fronts of SDG 15.

 

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 15 (SDG 15), which focuses on protecting, restoring, and promoting sustainable use of terrestrial ecosystems, managing forests sustainably, combating desertification, halting and reversing land degradation, and halting biodiversity loss. Between 2015 and 2023, there have been 203,592 scientific publications exploring the role of AI in these areas. These studies highlight AI’s potential to monitor and manage natural resources more efficiently, analyze vast datasets for biodiversity conservation, and develop strategies to combat land degradation. AI technologies, such as remote sensing and machine learning, are used to track deforestation, assess ecosystem health, and predict the impacts of climate change on terrestrial habitats. Despite relatively low media exposure, with only 3,156 news articles, there is growing awareness of AI’s potential in environmental conservation. Furthermore, the development of 190 AI policies targeting SDG 15 demonstrates a commitment from policymakers to integrate AI into ecosystem management and conservation efforts, ensuring sustainable and resilient terrestrial ecosystems.

Looking ahead, the next 5 to 10 years are expected to see significant advancements in AI applications for terrestrial ecosystem management. AI-driven remote sensing technologies will become more sophisticated, allowing for real-time monitoring of forests and other critical habitats, enhancing efforts to combat illegal logging and poaching. Machine learning algorithms will be increasingly used to analyze complex ecological data, providing insights into species distribution, habitat connectivity, and the impacts of human activities on biodiversity. These advancements will facilitate more effective conservation planning and policy-making. Additionally, AI can support reforestation and land restoration projects by optimizing the selection of plant species and monitoring the progress of restoration efforts. As AI technologies continue to evolve, collaboration between governments, conservation organizations, and the private sector will be essential to ensure that AI-driven innovations are implemented ethically and effectively. This collaborative effort will accelerate progress towards SDG 15, promoting the sustainable use and restoration of terrestrial ecosystems and biodiversity conservation.

 

Developed in collaboration with the European Commission project 

Here is your window to how AI is impacting above water life worldwide. 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 

BIAS BOARD

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 15.

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