How to Prevent Discriminatory Outcomes in Machine Learning

The opportunities that artificial intelligence (AI) can unlock for our world -- from discovering cures to diseases that kill millions each year to significantly reducing carbon emissions -- are expanding every day -- and is already enabling pathways to financial inclusion, citizen engagement, more affordable healthcare, and many more vital systems and services. The same types of machine learning systems that might have highlighted a certain post in your Facebook newsfeed based on your online activity are being leveraged, for instance, to highlight certain applicants in a hiring process. While public attention often focuses either on the existential threats artificial super-intelligence poses to humanity ("the robots are coming to kill us"), or the opposite salvation narrative (" AI will solve all our problems") there is a more immediate-but less visible- risk that our reliance on ML-driven decision making poses in terms of the reinforcement of systemic bias and discrimination. Machine learning technologies are already making life-altering decisions for human lives on a daily basis. Examples come from the New York Times: "Algorithms can decide where kids go to school… where building code inspections should be targeted, and even what metrics are used to rate a teacher."