New research looks to root out bias in artificial intelligence

It essentially boils down to the idea that "if there is an advantage to something that will be perceived as unethical, then it is quite likely the machine learning is going to find it," says Robert MacKay, a mathematician at the University of Warwick and an author of the paper. MacKay uses the example of an algorithm that prices insurance products. If it is optimized only to maximize revenue, it's likely to treat customers unfairly and even unlawfully, selecting a higher price for users whose names code as non-white. In their paper, MacKay and his colleagues lay out complex mathematics that can help businesses and regulators detect the unethical strategies an algorithm might pursue in a given space and identify how the AI should be modified to prevent that behavior. It essentially boils down to the idea that "if there is an advantage to something that will be perceived as unethical, then it is quite likely the machine learning is going to find it," says Robert MacKay, a mathematician at the University of Warwick and an author of the paper.