In short, no—at least when it comes to lower-income countries and populations, and not in time for the 2030 Sustainable Development Goals (SDGs). Even worse, it can be a major distraction from more urgent, foundational solutions critical to achieving the SDGs.
It’s 2019. So, it’s hard to get through a week without some news story about malevolent social media ‘bots, about how we are all going to live in a surveillance society, or generally about the imminent AI invasion which will take over all of humanity. On the rosier end of the spectrum, are the stories about the next new convenience AI will bring. There’s also the occasional story about how AI will end global poverty.
Over the past couple of years, the hype surrounding “AI for good”—among technology startups, philanthropic organizations, universities, and even governments and international institutions—has reached fever pitch. Most of these conversations are highly abstract, driven by a technology-first lens which fails to appreciate what types of interventions are foundational and urgent, and specifically how data analytics and AI can help.
Consider this example of an AI-driven solution highlighted at a recent UN summit on AI for development: “helping smallholder farmers in Sub-Saharan Africa improve crop yields with less water”. Of course, only good can come out of improving the efficiency of water use, if farmers are facing physical water scarcity. The problem with such a solution is two-fold. First, there are tried-and-true methods of improving water efficiency, such as drip irrigation. The challenge most farmers face is that they cannot afford these on-farm implements. Second, most smallholder farmers in Sub-Saharan Africa face economic water scarcity, i.e., there is often plenty of shallow groundwater in their general vicinity, but the farmers cannot afford the means of accessing that water through drills and pumps. Hence, they practice rainfed agriculture.
While there is no doubt that AI algorithms could improve water efficiency in modern irrigation systems, the problem that farmers in Sub-Saharan Africa face is more fundamental: limited resources to access water, let alone the equipment to use it efficiently. Similarly, using AI to develop accurate risk models for crop insurance will have little impact without the cash reserves to provide insurance in the first place, and without adequate systems for distributing insurance policies and processing claims.
Not surprisingly, the value that can be added by data analytics and AI in global development is highly context dependent. To unpack that context, our team at ITT has released a report that parses those nuances. In the report, we point out that:
Organizations focused on data and AI for the SDGs should, therefore, invest first in appropriate foundational systems and data infrastructures. Otherwise their impact will be as artificial as the intelligence.