Sometimes an image is worth a thousand words (and much more than a thousand data points). Explore the water sustainability from a worldwide data perspective and the progress we are making jointly towards a much improved water resource management.

This perspective offers a real-time view of the worldwide news on water-related topics, providing us with a media perspective on the progress at the different fronts of SDG 6.

 

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 influenced the progress of Sustainable Development Goal 6 (SDG 6), which aims to ensure the availability and sustainable management of water and sanitation for all. Between 2015 and 2023, there have been 1,118,737 scientific publications focused on the impact of AI on water and sanitation issues. These studies highlight AI’s potential in optimizing water resource management, predicting water quality, and enhancing the efficiency of water distribution systems. AI-driven models and algorithms can analyze vast datasets to identify patterns and anomalies in water usage, detect leaks, and predict the effects of climate change on water resources. Media exposure, with 88,675 news articles, underscores growing public interest and awareness of AI’s role in addressing water-related challenges. The development of 533 AI policies targeting SDG 6 indicates a commitment from policymakers to integrate AI technologies in water management practices, ensuring sustainable and equitable access to water and sanitation services.

Looking ahead, the next 5 to 10 years are poised to see further advancements in the application of AI to water and sanitation issues. We can expect the continued development of AI-powered tools for real-time monitoring and management of water quality and distribution, leading to more efficient and sustainable use of water resources. AI will play a critical role in predicting and mitigating the impacts of extreme weather events on water supply and infrastructure, enhancing resilience to climate change. Additionally, AI’s ability to optimize wastewater treatment processes will contribute to better sanitation practices and environmental protection. As AI technologies become more sophisticated and widely adopted, 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 help accelerate progress towards SDG 6, ensuring that clean water and sanitation are accessible to all, promoting health, and supporting sustainable development.

 

Developed in collaboration with the European Commission project 

Here is your window to Water Sustainability through AI. 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 6.

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