Improving Government Programs Relies on Data Access

Machine Learning in the Social Sector

Giving Compass' Take: • Ben Brockman, Andrew Fraker, Jeff McManus, and Neil Buddy Shah discuss the ways in which machine learning can boost the work of the social sector to increase impact.  • How is machine learning being applied in your issue area(s)? How can your work be augmented by machine learning?  • Learn about the potential for machine learning to shape better plant conservation.  The next big thing in the social sector has officially arrived. Machine learning is now at the center of international conferences, $25 million dollar funding competitions, fellowships at prestigious universities, and Davos-launched initiatives. Yet amidst all of the hype, it can be difficult to understand which social sector problems machine learning is best positioned to solve, how organizations can practically use it to enhance their impact, and what kind of sector-wide investments can enable the ambitious use of it for social good in the future. Our work at IDinsight, a nonprofit that uses data and evidence to help leaders in the social sector combat poverty, and the work of other organizations offer some insights into these questions. Machine learning uses data (usually a lot) and statistical algorithms to predict something unknown. In the private sector, for example, ride sharing apps use traffic data to predict customer wait times. Online streaming companies use customer history to predict which videos customers will want to watch next. In the social sector, machine learning is particularly ripe for use in addressing two kinds of problems. The first is prevention problems. If an organization focused on conflict resolution can predict where violent conflict is likely to breakout, for example, it can double-down on peacebuilding interventions. If a health NGO can predict where disease is most likely to spread, it can prioritize distribution of public health aid. The second is data-void problems. The data governments and nonprofits use to target social programs is rarely granular, recent, or accurate enough to pinpoint the specific regions or communities that would benefit most, and collecting more-comprehensive data is often prohibitively expensive. As a result, many of the people who need a program the most don’t receive it, and vice versa. If, however, an NGO fighting hunger in a rural state in Ethiopia knows which villages have the highest malnutrition rates, it can focus its outreach efforts in those communities, instead of oversaturating a different region that has fewer needs. Read the full article about machine learning by Ben Brockman, Andrew Fraker, Jeff McManus, and Neil Buddy Shah at Stanford Social Innovation Review.