Artificial intelligence has had some justifiably bad press recently. Some of the worst stories have been about systems that exhibit racial or gender bias in facial recognition applications or in evaluating people for jobs, loans, or other considerations. One program was routinely recommending longer prison sentences for blacks than for whites on the basis of the flawed use of recidivism data.

But what if instead of perpetuating harmful biases, AI helped us overcome them and make fairer decisions? That could eventually result in a more diverse and inclusive world. What if, for instance, intelligent machines could help organizations recognize all worthy job candidates by avoiding the usual hidden prejudices that derail applicants who don’t look or sound like those in power or who don’t have the “right” institutions listed on their résumés? What if software programs were able to account for the inequities that have limited the access of minorities to mortgages and other loans? In other words, what if our systems were taught to ignore data about race, gender, sexual orientation, and other characteristics that aren’t relevant to the decisions at hand?

AI can do all of this — with guidance from the human experts who create, train, and refine its systems. Specifically, the people working with the technology must do a much better job of building inclusion and diversity into AI design by using the right data to train AI systems to be inclusive and thinking about gender roles and diversity when developing bots and other applications that engage with the public.

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