Gender Bias in AI and Machine Learning Systems

Every year, Amazon gets more than 200 thousand resumes for the various jobs they are hiring for. Google, gets 10 times that, with over 2 million resumes each year. Imagine being the HR managers responsible for vetting all those. That seems like an absolutely daunting task, but, in the modern age we live in, this seems like a task that could be given over to something that could process those resumes nearly instantaneously: an artificial intelligence system. In fact, that’s exactly what companies like Amazon and Google have tried in the past, though the results were not what they expected.
Welcome to Data Demystified. I’m Jeff Galak and in this episode we’re going to talk about gender bias in artificial intelligence. To be sure, there are many examples of bias in machine learning and AI systems and I plan to make videos about those too, but, for now, I want to focus on one big example in the world of resume vetting. After all, one of the goals of things like gender and racial equity is to ensure that everyone, regardless of their gender or race, has a fair shake at the most desirable jobs out there. But when companies let AI algorithms have a say in those decisions, bias has a sneaky way of creeping in. In this episode, I’m going to try and provide you with the intuition to understand how this type of bias could emerge, even when a big goal of these systems is to take potentially biased humans out of the equation all together.
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