AI can’t solve social problems

With any new technological development, it is easy to wax poetic about the ways it can solve society’s ills—or hit every nail with your new hammer. Such optimism for AI’s potential is admirable, but it tends to ignore biases in AI. These biases range from frustrating, like Snapchat’s AI failing to recognize African American faces, to life endangering. Amazon’s Rekognition AI falsely identified 28 sitting members of Congress as having been previously arrested, with people of color matched at twice the proportional rate of their representation. This threatens to further reinforce biases against people of color, even though AI is thought to be impartial. The Congressional Black Caucus wrote to Jeff Bezos: “It is quite clear that communities of color are more heavily and aggressively policed than white communities.”

The caucus continued: “This status quo results in an oversampling of data which, once used as inputs to an analytical framework leveraging artificial intelligence, could negatively impact outcomes in those oversampled communities”. In using AI we need to recognize that it is not an impartial arbiter of justice, capable of distilling moral truth from data alone. These conceptions of right and wrong come from us and from the data we choose to provide. Without careful input and monitoring, an AI will simply reinforce the societal biases and structures used in its training.

Now, one could argue that these are not problems with AI, but problems with the data, and that a properly made AI shouldn’t have bias. We could theoretically make an AI with data sets not filled with moral judgments. But, even in creating such data sets we would be applying judgment over what constitutes “moral.” We can’t separate AI tools and data from the society that shapes them. Moreover, by applying AI to a value-laden problem, we make the mistake of assuming that social and ethical problems have technical solutions. With AI so entrenched in our everyday lives, we are seeing such events play out now.

Connecterra is trying to use TensorFlow to address global hunger through AI-enabled efficient farming and sustainable food development. The company uses AI-equipped sensors to track cattle health, helping farmers look for signs of illness early on. But, this only benefits one type of farmer: those rearing cattle who are able to afford a device to outfit their entire herd. Applied this way, AI can only improve the productivity of specific resource-intensive dairy farms and is unlikely to meet Connecterra’s goal of ending world hunger.

This solution, and others like it, ignores the wider social context of AI’s application. The belief that AI is a cure-all tool that will magically deliver solutions if only you can collect enough data is misleading and ultimately dangerous as it prevents other effective solutions from being implemented earlier or even explored. Instead, we need to both build AI responsibly and understand where it can be reasonably applied.