Women are still greatly underrepresented in STEM. The latest figures from the Women Tech Network show women only make up around 26-27% of the STEM workforce and the organization estimates that at the current rate of change, it will be nearly 123 years before the economic gender gap is closed.
It’s International Women’s Day 2026 – and the rate of women hired in tech continues to lag far behind that of men.
As hiring teams turn to AI tools to automatically field candidates, we’re at something of a crossroads – do we fix the bias inherent in hiring? Or simply replicate it at scale with similarly biased AI tools?
In this episode, Jane are Rory are joined by Clare Hickie, EMEA CTO at Workday, to discuss how businesses can engage in bias-free talent acquisition in the age of AI.
Highlights
“It’s not a problem if you control it, and the only way that you can control it is to ensure that you have got a responsible AI program, the right measures, the right procedures, the right guardrails and the right protocols in place.”
“I’ve been in technology a long, long time, and it’s now it’s my job to really pay back. And my job in paying back is by ensuring that I can go out and really encourage and inspire young people into technology. So there’s a couple of things going on.”
“We’ve seen a negative impact on society as a consequence of AI as well, right? But what we’re really are driving for is that positive impact on society, and the only way you can do that is removing the bias of the past. And the way you remove that biased information is to use the technology to also ensuring that you’re operating with clean data and with context.
“You have to apply fair processes. You have to apply, apply fair procedures. So in the past, organizations, for example, didn’t even have to post that there was another job, it was a tap on the shoulder after another tap on the shoulder. And sometimes, you know what? It worked and then for many they were just not even considered, because they were just forming these groups and teams and leadership teams that they were all from themselves.
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