The first computer algorithm is said to have been written in the early 1840s, for the prototype of the Analytical Engine. Ada Lovelace, a mathematician dubbed a "female genius" in her posthumous biography. As the field of computing developed over the next century following Lovelace's death, the typing work involved in creating computer programs was seen as "women's work," a role viewed as akin to switchboard operator or secretary. Women wrote the software, while men made the hardware -- the latter seen, at the time, as the more prestigious of the two tasks. And, during the Space Race of the 1950s and '60s, three Black women, known as "human computers," broke gender and racial barriers to help NASA send the first men into orbit.

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.