With suicide prevention, every minute of response time matters. That’s why the technology team at the well-known nonprofit Crisis Text Line in New York City analyzed some 65 million text messages to determine what words were most statistically associated with a high risk of suicide. This scale of analysis would clearly be infeasible without some form of automated analysis, and its results surprised the team. Use of the term “EMS” in a text, for example, is five times more predictive of a high risk of suicide than the actual word “suicide.” By using this analysis, the team can now better prioritize incoming messages, much like the triage system in a hospital emergency department. As a result, the organization is now able to respond to 94 percent of high-risk texters in fewer than five minutes.
This is just one example of “mission-driven artificial intelligence”—the responsible application of artificial intelligence (AI) to solve societal and ecological challenges. Sometimes dubbed “AI for good,” mission-driven AI is the use of machine-learning techniques to streamline operations and enhance programs at nonprofits, nongovernmental organizations (NGOs), and social enterprises.
To be clear, AI is a broad term that captures the constantly evolving advances in machines’ capabilities to perform tasks that would ordinarily require human intelligence. Machine learning is a specific approach to AI that applies statistical techniques to data in order to train computers to perform tasks without explicit human programming of rules. “Deep learning” is, in turn, a type of machine learning that relies on techniques that learn multiple layers of representations, in a way that mimics how the brain processes data. In this article, we refer to AI to intentionally focus on the broader picture. Machine learning does account for the vast majority of AI research and development today, however, and we use this term when referring to specific projects that make use of this technique.
Using AI in a mission-driven context could supercharge the capacities of the social change sector. Specifically, it has the potential to lower costs, improve quality, and broaden the impact of social change organizations. Think of it as transforming these organizations from a VW Beetle into the USS Enterprise.
The first step
We are now witnessing a Cambrian Explosion in machine learning, as algorithms defeat world champion Go players, drive cars, perform real-time translation via smartphone cameras, and streamline business processes that are already highly optimized—like reducing Google’s data center cooling bill by 40 percent.
These innovations will transform the nonprofit sector too. We predict that mission-driven organizations will initially adopt AI to build organizational capacity—improving effectiveness and sustainability by strengthening fundraising, marketing, administration, and other skills, processes, and resources. Capacity-building investments are often similar across organizations. One fundraising, accounting, or communications solution can, with a bit of tweaking, often meet the needs of many organizations. As a result, technology investments in capacity building can spread quite quickly through the nonprofit sector, and we expect this will be the case with machine learning-based solutions.