By weaving together an unusual array of data into a simple measure of a community's vulnerability to COVID-19, health care practitioners can develop tailored interventions to help Black Americans, who disproportionately bear the burden of the pandemic.
Using artificial intelligence to predict behavior can lead to devastating policy mistakes. Health and development programs must learn to apply causal models that better explain why people behave the way they do to help identify the most effective levers for change.
Machine learning is neither a panacea nor as technically daunting as some believe. When it comes to global development, the key is to ask the right questions, and then see if and how it can help.
It is imperative to include customers in solving problems, but jumping straight into human-centered design misses the rich and insightful data required to shift the system and achieve better results.
Most global development programs still segment people by demographics when trying to change their behavior. We must learn from the private sector and segment people based on the reasons behind their actions. Open access to this article is made possible by The Surgo Foundation.
