By analyzing data on traffic patterns and urban density, AI can contribute to lessening traffic congestion and enhancing urban planning. In this chart we see the mortality rate attributed to household and ambient air pollution, age-standardized (per 100,000 population).

This perspective offers a real-time view of the worldwide news on community-related topics, providing us with a media perspective on the progress at the different fronts of SDG 11.

 

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 been instrumental in advancing Sustainable Development Goal 11 (SDG 11), which aims to make cities and human settlements inclusive, safe, resilient, and sustainable. Between 2015 and 2023, there have been 253,595 scientific publications examining the role of AI in urban development and management. These studies highlight AI’s potential to improve urban planning, enhance public transportation systems, and optimize resource management. AI technologies can analyze vast amounts of data to predict and mitigate the effects of urban challenges such as traffic congestion, pollution, and energy consumption. Despite the relatively lower media exposure, with 6,364 news articles, public awareness of AI’s role in transforming urban environments is growing. The development of 561 AI policies targeting SDG 11 reflects a significant commitment from policymakers to integrate AI into urban development strategies, ensuring sustainable and resilient cities.

Looking ahead, the next 5 to 10 years are expected to witness significant advancements in AI applications for urban sustainability. AI-driven smart city initiatives will become more prevalent, enabling real-time monitoring and management of urban infrastructure. These smart cities will leverage AI to enhance public services, improve emergency response times, and reduce environmental impact. AI will also play a critical role in advancing sustainable mobility solutions, such as autonomous vehicles and intelligent public transport systems, reducing congestion and emissions. As AI technologies evolve, they will facilitate more effective disaster risk management and climate resilience strategies, ensuring that cities can adapt to changing environmental conditions. Collaboration between governments, urban planners, and technology companies will be essential to ensure that AI-driven innovations contribute to inclusive, safe, and sustainable urban development, aligning with the goals of SDG 11.

 

Developed in collaboration with the European Commission project 

Here is your window to the state of the world in regards to sustainable communities 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 11.

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