AI can enhance supply chain efficiency and decrease waste by analyzing data related to production and consumption patterns. In this chart we observe the wage and salaried workers, total (% of total employment) (modeled ILO estimate).

This perspective offers a real-time view of the worldwide news on socioeconomic-related topics, providing us with a media perspective on the progress at the different fronts of SDG 8.

 

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 played a transformative role in advancing Sustainable Development Goal 8 (SDG 8), which aims to promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. Between 2015 and 2023, there have been 587,187 scientific publications exploring the impact of AI on economic growth and employment. These studies highlight AI’s potential to boost productivity, drive innovation, and create new job opportunities. AI technologies have been instrumental in optimizing business processes, enhancing decision-making, and automating repetitive tasks, leading to increased efficiency and economic growth. Media exposure, with 35,075 news articles, indicates a significant public interest in the role of AI in shaping the future of work and economic development. The development of 558 AI policies targeting SDG 8 reflects a concerted effort by policymakers to ensure that AI-driven economic growth is inclusive and sustainable, addressing challenges such as job displacement and the digital divide.

In the next 5 to 10 years, AI’s impact on economic growth and employment is expected to deepen further. We can anticipate more widespread adoption of AI across various industries, leading to the creation of new job roles and the transformation of existing ones. AI-driven automation will continue to enhance productivity, allowing businesses to scale operations and innovate at a faster pace. However, this will also require a focus on reskilling and upskilling the workforce to ensure that workers are equipped with the skills needed to thrive in an AI-driven economy. Policymakers will need to develop strategies to mitigate the potential negative impacts of AI on employment, such as job displacement, by promoting inclusive education and training programs. Additionally, AI can support the growth of small and medium-sized enterprises (SMEs) by providing them with advanced tools and insights, fostering entrepreneurship and economic resilience. Overall, AI will play a crucial role in driving sustainable and inclusive economic growth, contributing significantly to the achievement of SDG 8.

 

Developed in collaboration with the European Commission project 

Here is your window to how AI is affecting Economic Growth. 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 8.

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