AI and the tradeoff between fairness and efficacy: 'You actually can get both'

A recent study in Nature Machine Intelligence by researchers at Carnegie Mellon sought to investigate the impact that mitigating bias in machine learning has on accuracy. Despite what researchers referred to as a "commonly held assumption" that reducing disparities requires either accepting a drop in accuracy or developing new, complex methods, they found that the trade-offs between fairness and effectiveness can be "negligible in practice." "You actually can get both. You don't have to sacrifice accuracy to build systems that are fair and equitable," said Rayid Ghani, a CMU computer science professor and an author on the study, in a statement. At the same time, Ghani noted, "It does require you to deliberately design systems to be fair and equitable.