05/12/2021

Investing in AI for Good

A new framework for identifying the biggest AI opportunities for social impact

In the past 10 years, hundreds of projects have applied artificial intelligence (AI) to creating social good. The right tool applied to an appropriate problem has the potential to drastically improve millions of lives through better service delivery and better-informed policy design. But what kind of investments do AI solutions need to be successful, and which applications have the most potential for social impact? 

Characteristics of Strong AI-for-good Investments

AI excels at helping humans harness large-scale or complex data to predict, categorize, or optimize at a scale and speed beyond human ability. We believe that more targeted, sustained investments in AI for social impact (sometimes called “AI for good”)—rather than multiple, short-term grants across a variety of areas—are important for two reasons. First, AI often has large upfront costs and low ongoing or marginal costs. AI systems can be hard to design and operationalize, and they require an array of potentially costly resources—such as training data, staff time, and high-quality data infrastructure—to get off the ground. Compared to the upfront investment, the cost of reaching each additional user is small. For philanthropies looking to drive positive social impact via AI, this often means that AI solutions must reach significant scale before they can offer a substantial social return on investment.

Another reason why targeted, sustained funding is important is because any single point of failure—lack of training data, misunderstanding users' needs, biased results, technology poorly designed for unreliable Internet—can hobble a promising AI-for-good product. Teams using AI need to continually refine and maintain these systems to overcome obstacles, achieve scale and maintain the ecosystems in which they live.

Please select this link to read the complete article from SSIR.