Jun 25, 2018

Deirdre Mulligan on Ethics and Machine Learning Algorithms: “Data isn’t Fair”

From the Fortune

Unmasking A.I.'s Bias Problem

By Jonathan Vanian

When Tay made her debut in March 2016, Microsoft had high hopes for the artificial intelligence–powered “social chatbot.” Like the automated, text-based chat programs that many people had already encountered on e-commerce sites and in customer service conversations, Tay could answer written questions; by doing so on Twitter and other social media, she could engage with the masses...

In less than a day, Tay’s rhetoric went from family-friendly to foulmouthed; fewer than 24 hours after her debut, Microsoft took her offline and apologized for the public debacle...

The most powerful algorithms being used today “haven’t been optimized for any definition of fairness,” says Deirdre Mulligan, an associate professor at the University of California at Berkeley who studies ethics in technology. “They have been optimized to do a task.” A.I. converts data into decisions with unprecedented speed—but what scientists and ethicists are learning, Mulligan says, is that in many cases “the data isn’t fair.”

Adding to the conundrum is that deep learning is much more complex than the conventional algorithms that are its predecessors—making it trickier for even the most sophisticated programmers to understand exactly how an A.I. system makes any given choice...

The dilemma for tech companies isn’t so much a matter of tweaking an algorithm or hiring humans to babysit it; rather, it’s about human nature itself. The real issue isn’t technical or even managerial—it’s philosophical. Deirdre Mulligan, the Berkeley ethics professor, notes that it’s difficult for computer scientists to codify fairness into software, given that fairness can mean different things to different people. Mulligan also points out that society’s conception of fairness can change over time. And when it comes to one widely shared ideal of fairness—namely, that everybody in a society ought to be represented in that society’s decisions—historical data is particularly likely to be flawed and incomplete.

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Deirdre K. Mulligan is an Associate Professor in the School of Information at UC Berkeley, a faculty Director of the Berkeley Center for Law & Technology, and an affiliated faculty of the Berkeley Center for Long-Term Cybersecurity.

Last updated:

July 31, 2018