News article by Karen Hao.
Published in MIT Technology Review.
There’s more talk of AI ethics than ever before. But talk is just that—it’s not enough.
Last year, just as I was beginning to cover artificial intelligence, the AI world was getting a major wake-up call. There were some incredible advancements in AI research in 2018—from reinforcement learning to generative adversarial networks (GANs) to better natural-language understanding. But the year also saw several high-profile illustrations of the harm these systems can cause when they are deployed too hastily.
A Tesla crashed on Autopilot, killing the driver, and a self-driving Uber crashed, killing a pedestrian. Commercial face recognition systems performed terribly in audits on dark-skinned people, but tech giants continued to peddle them anyway, to customers including law enforcement. At the beginning of this year, reflecting on these events, I wrote a resolution for the AI community: Stop treating AI like magic, and take responsibility for creating, applying, and regulating it ethically.
In some ways, my wish did come true. In 2019, there was more talk of AI ethics than ever before. Dozens of organizations produced AI ethics guidelines; companies rushed to establish responsible AI teams and parade them in front of the media. It’s hard to attend an AI-related conference anymore without part of the programming being dedicated to an ethics-related message: How do we protect people’s privacy when AI needs so much data? How do we empower marginalized communities instead of exploiting them? How do we continue to trust media in the face of algorithmically created and distributed disinformation? [ . . . ]