At the Raspberry Pi Foundation, we've been thinking about questions relating to artificial intelligence (AI) education and data science education for several months now, inviting experts to share their perspectives in a series of very well-attended seminars. At the same time, we've been running a programme of research trials to find out what interventions in school might successfully improve gender balance in computing. We're learning a lot, and one primary lesson is that these topics are not discrete: there are relationships between them. We can't talk about AI education -- or computer science education more generally -- without considering the context in which we deliver it, and the societal issues surrounding computing, AI, and data. For this International Women's Day, I'm writing about the intersection of AI and gender, particularly with respect to gender bias in machine learning.

Any examination of bias in AI needs to recognize the fact that these biases mainly stem from humans' inherent biases. The models and systems we create and train are a reflection of ourselves. So it's no surprise to find that AI is learning gender bias from humans. For instance, natural language processing (NLP), a critical ingredient of common AI systems like Amazon's Alexa and Apple's Siri, among others, has been found to show gender biases – and this is not a standalone incident. There have been several high profile cases of gender bias, including computer vision systems for gender recognition that reported higher error rates for recognizing women, specifically those with darker skin tones.