https://phys.org/news/2018-07-algorithm-limits-bias-machine.html

In a new paper published in the Proceedings of the 35th Conference on Machine Learning, SFI Postdoctoral Fellow Hajime Shimao and Junpei Komiyama, a research associate at the University of Tokyo, offer a way to ensure fairness in machine learning. They've devised an algorithm that imposes a fairness constraint that prevents bias.

"So say the credit card approval rate of black and white [customers] cannot differ more than 20 percent. With this kind of constraint, our algorithm can take that and give the best prediction of satisfying the constraint," Shimao says. "If you want the difference of 20 percent, tell that to our machine, and our machine can satisfy that constraint."