What We Do

What we do

AI For Good’s mission is to drive forward technological solutions that measure and advance the UN’s Sustainable Development Goals. We do this by bringing together a broad network of interdisciplinary researchers, nonprofits, governments, and corporate actors to identify, prototype and scale solutions that engender positive social change.

Here is a brief summary of the research, tools, programs and policy-development we have been working on over the last two years.


The AI for Good Foundation addresses three principal issues that exist today, and that are preventing Artificial Intelligence from having its most beneficial impact on society.

  • The lack of a common vision in the AI research community, and a fragmented body of scientific work that is difficult to reproduce and build upon;
  • The absence of mechanisms and forums of communication between researchers, practitioners, policy-makers, and the public, to educate and engage on the opportunities and threats of emerging capabilities, both from a technology and socio-economic perspective;
  • The unbalanced nature of incentives and funding for Artificial Intelligence research, that end up favoring defense and military applications, at the expense of directly socially beneficial projects;

We believe that Artificial Intelligence is misunderstood, that the various stakeholders do not communicate as well as they should, and that the research efforts need more common ground for sharing and benefiting from the good work that is already happening. We also believe that the solutions need to be global, bringing together people from many different backgrounds, identifying the most promising areas for research and implementation, and being the voice of rationality and data-driven conclusions. As such, we have developed a series of complementary programs, engaging the world’s most prominent researchers in the field, and based on a culture of evidence-driven transparency. Our programs are:

SDG Data Catalogue

Modern scientific research for Sustainable Development depends on the availability of large amounts of relevant real-world data. Despite this need, there are currently no extensive global databases that associate existing data sets with the research domains they cover. The SDG Data Catalogue is an open, extensible, global database of data sets, metadata, and research networks built automatically by mining millions of published open access academic works. The system leverages advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP) technologies to extract and organize deep knowledge of datasets available that is otherwise hidden in plain sight in the continuous stream of research generated by the scientific community. The goal, ultimately, is to connect researchers and students with SDG-relevant datasets so that their work can make meaningful progress towards social good.

Sustainable AI Policy

The AI for Good Foundation collaborates with policy groups, think tanks, and nonprofits to assist in developing AI policy frameworks for regional, national, and international governmental agencies. These collaborations provide customized recommendations for responsible AI growth and utilization to the organizations and governments with whom we partner. Our passion is to work with developing nations to design national AI policy frameworks by which they can meet the needs of their populace. Additionally, having a national AI policy in place gives developing nations the road map by which to navigate interests from multinational corporations and cross border collaborations. By sharing our expertise in this format, we are able to further the UN Sustainable Development Goals, as well as demonstrate our “zero-footprint” AI model.

Workforce, Diversity + AI

In 2018 the AI for Good Foundation, Fordham University, and Genos International conducted a broad assessment of the public perception of Artificial Intelligence in the workforce as the “Global Perceptions of AI” survey. The study found that many people are actively concerned and fearful of AI’s impact and believe their jobs to be at risk in the short and medium term. A significant driver of this sentiment is negative media coverage, as well as films and political statements. Since then, the AI for Good Foundation has been collaborating on several fronts to develop policy-relevant research, events, and thought-leadership. In general, there is little empirical evidence of any widespread negative impact on labor markets due to the introduction of Artificial Intelligence technologies in the last decade. There is evidence for a positive and statistically significant impact on employment, firm growth, and industry concentration, which is well aligned with previous waves of technology innovation and adoption.

On the other hand, we see potential for Artificial Intelligence to bring transparency to labour markets, hiring practices, and encourage companies to embrace diverse and inclusive workforces. We are collaborating with UC Berkeley to create global company diversity scorecards that empower companies and employees to understand the progress they are making, and provide best practices and supporting resources to accelerate innovation.

SDG Progress Tracker

AI for Good and our project partners are leveraging AI and web-scale data to keep track of progress towards the United Nations’ Sustainable Development Goals in real time. In partnership with the UNDP and RISE Research Institutes of Sweden, the AI for Good Foundation develops Artificial Intelligence algorithms capable of reading the web at scale, and identifying solutions and progress on the SDG’s. Check out the SDG Trend Scanner Project, and stay tuned for Climate-specific tracking tools, and SDG sub category directed data projects.

AI + Innovative Health

How can we use AI and Machine learning in a holistic way that supports non-invasive medicine and costly lab-based specialty research? Can we build an ecosystem where researchers can augment their research with global natural experiments? AI for Good and our partners seek to support the UN’s Sustainable Development Goals related to health through a lense of patient care, healthcare cost, and innovative research. This topic is also featured in our policy work for local and national governments. AI for Good considers public health, healthcare systems, and individual health in our customized AI Policy Recommendations.

AI+SDGs Launchpad

The AI+SDGs Launchpad allows any school, college, university, or research institute to easily create and manage a curriculum that bridges the gap between the “Data-enabled Sciences” and the United Nations’ Sustainable Development Agenda for 2030. The Launchpad is a blueprint for single or multi-semester courses that allow technically-trained students to engage on global challenges they are passionate about in a structured way.

The AI for Good Foundation provides mentoring connections to NGO’s, government, policy groups, and social benefit corporations, along with data, background research, and a proven course structure that delivers measurable and repeatable exposure to using AI for social good in a collaborative setting. Our aim is to enable any organisation to quickly build impactful curriculum to provide valuable training and inspiration to the next generation of data-driven scholars.

The initial idea stems from high-level workshops at Stanford University in 2014, which has since led to pilot courses in over 5 countries. Students collaborate on topics that pertain to their interests in AI, Machine-Learning and Data Science, as well as SDG topics such as urban development, media bias, and plastic pollution. Participants are given the opportunity to be a part of the international alumni of this on-going program and stay connected to share concepts and research as they grow in their careers.

The Intelligent Cities Project (ICP)

The “AI self-assessment tool” is for city administrations to understand the areas where AI will likely affect their specific region and population in the coming years. Our web-based survey generates a series of recommendations specific to the demographics and needs of an urban area. The recommendations we make come with links to resources and organisations that can help with scalable programme implementation.The goals of this project are two-fold: this easy introductory step can help Intelligent Cities plan for the future and explore how Artificial Intelligent will shape their environment. Second, this initial engagement will lead to more policy engagement, and ecosystem building between local and national governments, AI for Good, and our partners. We will host ‘Intelligent Cities’ summits where leaders from each city can gather to share knowledge and best practices with their counterparts across the globe.

Fragile Earth

Since 2016 the AI for Good Foundation has organised the Fragile Earth community and associated events, bringing together researchers, subject matter experts, governments, and policy groups to learn and discuss how Artificial Intelligence can help address problems with the Earth’s Biome and threats to its stability. AI for Good has partnered with IBM, the University of Minnesota, the University of Southern California, Northeastern University, Big Data Hubs, Syngenta, Cargill, the Santa Fe Institute, the ACM, the American Association for the Advancement of Science, and a variety other organisations in order to develop workshops, datasets, community engagement, and research that can have a direct impact on these themes.

Financial Data Science Association ( FDSA)

AI for Good’s Financial Data Science Association organizes international summits and research at the intersection of data, Artificial Intelligence, and global finance. We bring together top leaders across the financial industry to build and share best practices, and accelerate the appropriate use of Artificial Intelligence for increasing financial inclusion, market efficiency, and transparency. Our approach creates buy-in for intelligent innovations from the highest levels of leadership in the financial industry, mobilising wide-spread change and mitigating often ‘entrenched’ attitudes towards technology-driven innovation.