AI is aiding in ocean monitoring, biodiversity preservation, and sustainable fisheries management through data analysis and predictive modeling In this chart we observe the sea water levels measured in the changes of the sea mean level (in mm) over time.

This perspective offers a real-time view of the worldwide news on life underwater-related topics, providing us with a media perspective on the progress at the different fronts of SDG 14.

 

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 contributed to the progress of Sustainable Development Goal 14 (SDG 14), which aims to conserve and sustainably use the oceans, seas, and marine resources. Between 2015 and 2023, there have been 1,597,296 scientific publications focused on the impact of AI on marine conservation and resource management. These studies underscore AI’s potential in monitoring marine ecosystems, predicting changes in ocean conditions, and optimizing fisheries management. AI technologies, such as machine learning and satellite imaging, can analyze large datasets to detect illegal fishing activities, track marine biodiversity, and assess the health of coral reefs. Despite the relatively lower media exposure, with 4,717 news articles, public awareness of AI’s role in marine conservation is gradually increasing. Additionally, the development of 296 AI policies targeting SDG 14 reflects a growing commitment from policymakers to integrate AI into marine conservation strategies, ensuring the sustainable use of ocean resources and the protection of marine habitats.

Looking ahead, the next 5 to 10 years are poised to see significant advancements in the application of AI for marine conservation and sustainable use of ocean resources. AI-driven technologies will become more sophisticated, enabling more accurate and real-time monitoring of marine environments. This will facilitate better management of marine protected areas, helping to preserve biodiversity and mitigate the impacts of climate change on oceans. AI will also play a crucial role in advancing sustainable fisheries by improving stock assessments and promoting efficient fishing practices, thereby reducing overfishing and supporting the livelihoods of coastal communities. As the understanding of AI’s potential in marine conservation grows, collaboration between governments, research institutions, and the private sector will be essential to ensure that AI-driven innovations are implemented effectively and ethically. This collaborative effort will accelerate progress towards SDG 14, fostering healthier and more resilient marine ecosystems for future generations.

 

Developed in collaboration with the European Commission project 

Here is your window to how AI is impacting below 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 

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 14.

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