Many programmers are looking over AI to uncover sources of bias and producing solutions.

The ImageNet team analyzed their data set to uncover these and other sources of bias, and then took steps to address them. They used crowdsourcing to identify and remove derogatory words. They also identified terms that project meaning onto an image, for example “philanthropist,” and recommended excluding the terms from AI training. Keep Reading illustration of a head The latest on artificial intelligence, from machine learning to computer vision and more The team also assessed the demographic and geographic diversity in the ImageNet photos and developed a tool to surface more diverse images. For instance, ordinarily, the term “programmer” might produce lots of photos of white men in front of computers. But with the new tool, which the group plans to release in coming months, a subset of images that shows greater diversity in terms of gender, race, and age can be generated and used to train an AI algorithm. The effort shows how AI can be reengineered from the ground up to produce fairer results. But it also highlights how dependent AI is on human training and shows how challenging and complex the problem of bias often is.