AI can help in poverty reduction by providing better access to information, resources, and services for vulnerable communities. In this chart we show the multidimensional poverty through the UN headcount ratio (% of population) in relation to the national AI policies affecting SDG 1.

This perspective offers a real-time view of the worldwide news on poverty-related topics, providing us with a media perspective on the progress at the different fronts of SDG 1.

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.

 

Artificial Intelligence (AI) is making significant strides in advancing Sustainable Development Goal 1 (SDG 1), which aims to eradicate poverty. Recent scientific publications, totaling 319,151 in this period, underscore the profound impact AI can have on poverty reduction. These studies explore how AI technologies are being leveraged to improve economic opportunities, enhance access to essential services, and optimize resource distribution in impoverished regions. The high volume of academic research indicates a growing recognition within the scientific community of AI’s potential to address the multifaceted challenges associated with poverty. Furthermore, AI-driven initiatives, such as predictive analytics for crop yields and personalized education platforms, are proving to be effective tools in creating sustainable economic opportunities and enhancing quality of life for impoverished populations.

The media exposure, with 134,644 news articles discussing AI’s impact on SDG 1, reflects a broad public and governmental awareness and interest in the role of AI in poverty alleviation. This significant media presence helps to drive policy-making and public support for AI-based solutions. Additionally, with 844 specific AI policies targeting the impact on SDG 1, there is a concerted effort by governments and organizations to harness AI for sustainable development. Looking ahead, the next 5 to 10 years are likely to see exponential growth in AI applications designed to tackle poverty. With continuous advancements in AI technology and increased policy support, we can anticipate more effective and widespread implementation of AI-driven solutions, leading to substantial progress in poverty reduction globally. This period is expected to witness enhanced AI integration into economic planning and social services, resulting in more targeted and efficient interventions to uplift impoverished communities.

Here is your window to the world progress in extinguishing poverty. 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.

 

 

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 1.

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