“AI is going to learn what we teach it, so if we have applications in the [Defense Department] where the humans that are involved in the process of training the data are all white 22-year-old soldiers, then there’s going to be bias in the datasets that end up in the algorithms,” Metzger said.
“I would argue that the problems in our AI systems are not simply limitations of data or developers’ blind spots, but rather stem from our entire approach to how these systems are built. We have approached AI development from the top-down, largely dictated by the viewpoints of developed nations and first-world cultures. No surprise then that the biases we see in the output of these systems reflect the unconscious biases of these perspectives.
Rather than top-down approaches that seek to impose a model on data that may be beyond its contexts, we should approach AI as an iterative, evolutionary system. If we flip the current model to be built-up from data rather than imposing upon it, then we can develop an evidence-based, idea-rich approach to building scalable AI-systems. The results could provide insights and understanding beyond our current modes of thinking.
The global problems we face today are unprecedented. They require recognition of new types of data, new methods of understanding information and new modes of thinking. AI is one of the best potential tools humanity has to confront these present and future challenges, but only if we don’t reinforce the mistakes of our past.”