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2,602 uses of AI for social good, and what we learned from them

September 12th, 2019

For the past few years, we’ve applied core Google AI research and engineering to projects with positive societal impact, including forecasting floods, protecting whales and predicting famine. Artificial intelligence has incredible potential to address big social, humanitarian and environmental problems, but in order to achieve this potential, it needs to be accessible to organizations already making strides in these areas. So, the Google AI Impact Challenge, which kicked off in October 2018, was our open call to organizations around the world to submit their ideas for how they could use AI to help address societal challenges.

Accelerating Insights from the Google AI Impact Challenge” sheds light on the range of organizations using AI to address big problems. It also identifies several trends around the opportunities and challenges related to using AI for social good. Here are some of the things  we learned—check out the report for more details.

AI is globally relevant 

We received 2,602 applications from six continents and 119 countries, with projects addressing a wide range of issue areas, from education to the environment. Some of the applicants had experience with AI, but 55 percent of not-for-profit organizations and 40 percent of for-profit social enterprises reported no prior experience with AI.

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Similar projects can benefit from shared resources

When we reviewed all the applications, we saw that many people are trying to tackle the same problems and are even using the same approaches to do so. For example, we received more than 30 applications proposing to use AI to identify and manage agricultural pests. The report includes a list of common project submissions, which will hopefully encourage people to collaborate and share resources with others working to solve similar problems.

You don’t need to be an expert to use AI for social good

AI is becoming more accessible as new machine learning libraries and other open-source tools, such as Tensorflow and ML Kit, reduce the technical expertise required to implement AI. Organizations no longer need someone with a deep background in AI, and they don’t have to start from scratch. More than 70 percent of submissions, across all sectors and organization types, used existing AI frameworks to tackle their proposed challenge.

Successful projects combine technical ability with sector expertise 

Few organizations had both the social sector and AI technical expertise to successfully design and implement their projects from start to finish. The most comprehensive applications established partnerships between nonprofits with deep sector expertise, and academic institutions or technology companies with technical experience.

ML isn’t the only answer 

Some problems can be addressed by using alternative methods to AI—and result in faster, simpler and cheaper execution. For example, several organizations proposed using machine learning to match underserved populations to legal knowledge and tools. While AI could be helpful, similar results could be achieved through a well-designed website. While we’ve seen the impact AI can have in solving big problems, you shouldn’t rule out more simple approaches as well.

Global momentum around AI for social good is growing—and many organizations are already using AI to address a wide array of societal challenges. As more social sector organizations recognize AI’s potential, we all have a role to play in supporting their work for a better world.