The problem with this kind of proxy, though, is that it relies on assumptions—and those assumptions get embedded more deeply over time. So if your model assumes, from what it has seen and heard in the past, that most people interested in technology are men, it will learn to code users who visit tech websites as more likely to be male. Once that assumption is baked in, it skews the results: the more often women are incorrectly labeled as men, the more it looks like men dominate tech websites—and the more strongly the system starts to correlate tech website usage with men.
