Artificial intelligence can be just as biased as human beings, which is why experts are trying to prevent discrimination in machine learning. In a new paper, three Google researchers note that there is no existing way to ensure—as the White House calls it—“equal opportunity by design,” but they have an idea.
“Despite the need, a vetted methodology in machine learning for preventing this kind of discrimination based on sensitive attributes has been lacking,” wrote Moritz Hardt, a research scientist with the Google Brain Team and co-author of the paper, in a blog post.
Hardt throws out two seemingly intuitive approaches, “fairness through unawareness” and “demographic parity,” but dismisses them for their respective loopholes. By learning from the shortcomings of the aforementioned methods, the team came up with a new approach. The core concept is to not use “sensitive attributes”—race, gender, disability, or religion—so that “individuals who qualify for a desirable outcome should have an equal chance of being correctly classified for this outcome.”
“We’ve proposed a methodology for measuring and preventing discrimination based on a set of sensitive attributes,” wrote Hardt, whose co-authors are his colleagues Eric Price and Nathan Srebro. “Our framework not only helps to scrutinize predictors to discover possible concerns. We also show how to adjust a given predictor so as to strike a better tradeoff between classification accuracy and non-discrimination if need be.”
Read more about it here: http://lat.ms/2ecrYc2