AI Learns Gender and Racial Biases from Language

Tech giants and startups that use machine learning--especially cutting-edge deep learning algorithms--will need to grapple with the potential biases in their AI systems sooner rather than later. So far there seems to be more growing awareness and discussion of the problem rather than any systematic agreement on how to handle bias in machine learning AI, Friedler explains. One approach involves scrubbing any biases out of the datasets used to train machine learning AI. But that may come at the cost of losing some useful linguistic and cultural meanings. People will need to make tough ethical calls on what bias looks like and how to proceed from there, lest they allow such biases to run unchecked within increasingly powerful and widespread AI systems. "We need to decide which of these biases are linguistically useful and which ones are societally problematic," Friedler says. "And if we decide they're societally problematic, we need to purposely decide to remove this information."