Artificial Intelligence for the Public Good

Artificial intelligence has tremendous public good potential. We’re trying to unlock that potential in a responsible, sustainable, and inclusive way.

The first step in putting artificial intelligence to work for public good is getting critical data into the right hands. Data can transform our approach to social, humanitarian, and sustainable development goals. In a natural disaster, privately-held mobility data can direct the flow of critical resources. A company’s satellite images and drone footage can predict areas at dire risk of flooding or wildfires. And data about ridesharing and bicycle rentals can inform municipal urban planning.

This kind of increased data exchange and reuse is both practically difficult and ethically risky in many contexts. We are interested in lowering barriers and mitigating risk when data is used for the public good. Our research explores a cohesive approach to global data governance that balances collective needs against the rights of individuals, the interests of commercial entities, and the risk to our most vulnerable populations.

We recognize that data is necessary, but not sufficient, for our goals. We also need to ensure efficient and responsible use of data to meet pressing social and humanitarian needs. Artificial intelligence promises to improve public service delivery, address health inequalities, and direct the allocation of scarce resources, among other things. Ethical guidance on the deployment of artificial intelligence for these purposes is limited but urgently needed. We study the unique risks involved in using artificial intelligence for public benefit, especially in contexts with limited access to quality data, expertise, and internet connectivity.

Partner With Us

We’re eager to partner with groups, institutions, and initiatives that are committed to the ethical and responsible deployment of artificial intelligence in humanitarian and sustainable development work. We’re especially interested in engaging with groups in low- and middle-income countries, or in the Majority World.