MIT Sloan Management Review Article on Manage AI Bias Instead of Trying to Eliminate It

Businesses and governments must face an uncomfortable truth: Artificial intelligence is hopelessly and inherently biased.

Asking how to prevent such bias is in many ways the wrong question, because AI is a means of learning and generalizing from a set of examples — and all too often, the examples are pulled straight from historical data. Because biases against various groups are embedded in history, those biases will be perpetuated to some degree through AI.

Traditional and seemingly sensible safeguards do not fix the problem. A model designer could, for example, omit variables that indicate an individual’s gender or race, hoping that any bias that comes from knowing these attributes will be eliminated. But modern algorithms excel at discovering proxies for such information. Try though one might, no amount of data scrubbing can fix this problem entirely. Solving for fairness isn’t just difficult — it’s mathematically impossible.

About the Author

Sian Townson is a partner in the digital practice of global consultancy Oliver Wyman. She holds a doctorate in mathematical modeling from Oxford.

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