AI is enabling data analysis and governance, promoting justice, and strengthening institutions for inclusive and peaceful societies. In the following we observe the intentional homicides counts (per 100,000 people).

This perspective offers a real-time view of the worldwide news on justice-related topics, providing us with a media perspective on the progress at the different fronts of SDG 16.

 

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 16 (SDG 16), which focuses on promoting peaceful and inclusive societies, providing access to justice for all, and building effective, accountable, and inclusive institutions at all levels. Between 2015 and 2023, there have been 168,364 scientific publications examining the role of AI in enhancing governance, improving legal systems, and fostering transparency and accountability. These studies highlight AI’s potential to detect and prevent corruption, analyze legal documents for judicial efficiency, and support data-driven decision-making in public administration. Despite relatively low media exposure, with only 6,431 news articles, there is a growing recognition of AI’s role in strengthening institutions and promoting justice. Furthermore, the development of 204 AI policies targeting SDG 16 reflects a commitment from policymakers to integrate AI into governance and legal frameworks, ensuring that AI technologies contribute to building more effective and inclusive institutions.

Looking ahead, the next 5 to 10 years are expected to witness significant advancements in AI applications for promoting peace, justice, and strong institutions. AI-driven tools for detecting fraudulent activities and monitoring government transactions will become more sophisticated, enhancing efforts to combat corruption and improve transparency. Machine learning algorithms will be increasingly used to analyze large volumes of legal texts, helping to streamline judicial processes and improve access to justice. AI will also play a crucial role in conflict prevention and resolution by analyzing social and political data to identify early warning signs of unrest and enabling timely interventions. As awareness of AI’s potential in promoting SDG 16 grows, collaboration between governments, civil society, and technology companies will be essential to ensure that AI-driven innovations are implemented ethically and responsibly. This collaborative effort will accelerate progress towards SDG 16, fostering more peaceful, just, and inclusive societies globally.

 

Developed in collaboration with the European Commission project 

Here is your window to how AI is impacting peace and justice 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 16.

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