When\u00a0Timnit Gebru\u00a0was a student at\u00a0Stanford University\u2019s prestigious Artificial Intelligence Lab, she ran a project that used Google Street View images of cars to determine the demographic makeup of towns and cities across the U.S.\u00a0 While the AI\u00a0algorithms did a credible job of predicting income levels and political leanings in a given\u00a0area,\u00a0Gebru\u00a0says her work was susceptible to bias\u2014racial, gender, socio-economic.\u00a0She\u00a0was also horrified by a\u00a0ProPublica\u00a0report that found\u00a0a computer program widely used to predict whether a criminal will re-offend\u00a0discriminated against people of color.
\nSo earlier this year, Gebru, 34, joined a\u00a0Microsoft Corp. team called\u00a0FATE\u2014for Fairness, Accountability, Transparency and Ethics in AI.\u00a0The program was set up three years ago to ferret out biases that creep\u00a0into AI data and can skew results.
\n\u201cI started to realize that I have to start thinking about things like bias,\u201d\u00a0says Gebru, who co-founded Black in AI, a group set up to encourage\u00a0people of color to join the artificial intelligence field. \u201cEven my own Phd work suffers from whatever issues you’d have with dataset bias.\u201d
\nIn the popular imagination, the threat from AI tends to the alarmist: self-aware computers turning on their creators and taking over the planet. The reality (at least for now) turns out to be a lot more\u00a0insidious but no less concerning to the people working in AI labs around the world. Companies, government agencies and hospitals are increasingly turning to machine learning, image recognition and other AI tools to\u00a0help\u00a0predict everything from the credit worthiness of a loan applicant to the preferred treatment for a person suffering from cancer. The tools have big blind spots\u00a0that particularly effect women and minorities.
\n\u201cThe worry is if we don’t get this right, we could be making wrong decisions that have critical consequences to someone’s life, health or financial stability,\u201d says Jeannette Wing, director of Columbia University’s Data Sciences Institute.
\nResearchers at Microsoft, International Business Machines Corp. and the University of Toronto\u00a0identified\u00a0the need for fairness in AI systems back in 2011. Now in the wake of several high-profile incidents\u2014including an AI beauty contest that chose predominantly white faces as winners\u2014some of the best minds in the business are working on the bias problem. The issue will be a key topic at the Conference on Neural Information Processing Systems, an annual confab that starts today in Long Beach, California, and brings together AI scientists from around the world.
\nRead the source article at Bloomberg Technology.
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