Mm-hmm

... purposes around 20% less than men, and that is a consistent figure that's, like, shows up across the research over and over again.

And I got really curious as to why that was.

Um, and there's this kind of, um...

There's this, um, narrative that it's like women are scared or women aren't good at tech, and I was like, "Mm, I kinda feel like it's more complicated than that." So I felt...

I went into the literature, and I felt, I found that there are so many very good reasons why women are skeptical.

You know, women ex- There's...

... of the jobs? Let's, let's talk about that figure for a moment.

Yes.

We can do a lot.

So, right.

So as I was identifying this issue, So I'm of the belief that it's very difficult to be a credible advocate on a particular topic if you don't understand that topic very deeply.

And so as I kind of saw this unfolding in AI, I was like, oh, I need to understand how these tools work.

What's the technical nature of how they're developed? Anyway, so I was looking for roots in as a non-technical person, and I found the roots that were available to me either very, not either, both very isolating and isolated and kind of landing in an impractical place, meaning I felt like I was asked to absorb all of this computer science kind of theory and When really I just kind of want, I mean, I need to know some of that as an advocate in the space.

I need to know some of that because then you have, then you identify how bias exists and is replicated, et cetera.

So can you give us a bit of a lowdown on like the ones that we should be Well, just your input on that.