Essay by Iqbal Dhaliwal: “Artificial intelligence (AI) has the potential to transform our lives. Like the internet, it’s a general-purpose technology that spans sectors, is widely accessible, has a low marginal cost of adding users, and is constantly improving. Tech companies are rapidly deploying more capable AI models that are seeping into our personal lives and work.
AI is also swiftly penetrating the social sector. Governments, social enterprises, and NGOs are infusing AI into programs, while public treasuries and donors are working hard to understand where to invest. For example, AI is being deployed to improve health diagnostics, map flood-prone areas for better relief targeting, grade students’ essays to free up teachers’ time for student interaction, assist governments in detecting tax fraud, and enable agricultural extension workers to customize advice.
But the social sector is also rife with examples over the past two decades of technologies touted as silver bullets that fell short of expectations, including One Laptop Per Child, SMS reminders to take medication, and smokeless stoves to reduce indoor air pollution. To avoid a similar fate, AI-infused programs must incorporate insights from years of evidence generated by rigorous impact evaluations and be scaled in an informed way through concurrent evaluations.
Specifically, implementers of such programs must pay attention to three elements. First, they must use research insights on where AI is likely to have the greatest social impact. Decades of research using randomized controlled trials and other exacting empirical work provide us with insights across sectors on where and how AI can play the most effective role in social programs.
Second, they must incorporate research lessons on how to effectively infuse AI into existing social programs. We have decades of research on when and why technologies succeed or fail in the social sector that can help guide AI adopters (governments, social enterprises, NGOs), tech companies, and donors to avoid pitfalls and design effective programs that work in the field.
Third, we must promote the rigorous evaluation of AI in the social sector so that we disseminate trustworthy information about what works and what does not. We must motivate adopters, tech companies, and donors to conduct independent, rigorous, concurrent impact evaluations of promising AI applications across social sectors (including impact on workers themselves); draw insights emerging across multiple studies; and disseminate those insights widely so that the benefits of AI can be maximized and its harms understood and minimized. Taking these steps can also help build trust in AI among social sector players and program participants more broadly…(More)”.