Just 5% of the leadership positions in tech are held by women. Do you think enough is being done to create more gender balanced leadership teams?
Given the statistics, the answer can only be that there is more to be done. But people are talking very openly now about the numbers, and about the problems and barriers, which is positive. Discrimination and exclusion are really complex issues, but very broadly speaking with women in tech there are two big challenges I think about a lot.
Firstly, meaningful change won’t happen unless big companies get on board. Some still simply don’t acknowledge the problem but I think many are moving in the right direction, with equal pay and women on boards for example. But it takes time in very large companies for initiatives to filter through, for senior management to turn the ship around and say: “we’re going to make a real commitment here to do this”, and then get on and do it. The intention is increasingly there, but it’s not happening fast enough. That’s the downstream problem.
There’s also an upstream problem, related to the gender stereotypes around STEM. Giving girls the confidence with STEM subjects at a really young age is critical, and we know that’s not happening enough. A career in ‘technology’ historically meant engineering, which was definitely a ‘non-traditional’ sector for women, and I think that misperception still dogs us. Girls have very few real role models too when it comes to technology – look at who runs the FAANG companies. PWC’s ‘Tech She Can’ campaign found that half the number of females have had a career in tech suggested to them, compared to males, and far fewer consequently picture themselves capable of doing it. We need teachers, executives, government, academia, all reinforcing their confidence at a really early stage, spotting the talent and nurturing it from there.
So there’s two different ends of the spectrum. In the boardroom, the will is there in a lot of cases I think, but it’s not happening fast enough; in the classroom we know what needs to be done but it’s a really big challenge against centuries of unfair stereotyping and exclusion.
I’m really fortunate to work in a business which is openly and ambitiously committed to all aspects of diversity, including gender. We have some inspirational female technology and data science leaders here, including our Global Senior Vice Presidents for Data, Security & Identity Products, and our Senior Vice President of Visa Research, where a lot of our cutting-edge AI is developed. And we have substantial global campaigns and initiatives to empower female business owners and entrepreneurs (particularly through the non-profit Visa Foundation). It’s all wrapped up in our commitment to supporting individuals and businesses go digital. We know those are the skills you need to succeed and thrive in the 21st century economy, and women are so often excluded from that education and support.
Do you get a sense things are changing?
I do think the tide is turning on these issues and women are making their voices heard in the sector. There is a real movement emerging by and for women in technology. There are extraordinarily courageous digital activists like Timnit Gebru, Safiya Noble, Cathy O’Neill and Joy Buolamwini, whose work is truly world-changing; and there are many fantastic networks like Women Leading in AI, which taught me the foundations of what I believe about AI ethics and diversity. These networks don’t just create professional networking opportunities (although those are very important), they campaign on these issues as well. They’re also not just talking about diversity and inclusivity of gender, but for all groups which are currently underrepresented or excluded. This makes sense from a fairness perspective in terms of equality of opportunity, not to mention the travesty of missed talent when we overlook or exclude these groups.
When you look at the representation of women and other minority groups in data and AI, we see a very stark link between that and our ability to mitigate bias in AI products. If you don’t have that diversity of experience, viewpoints, and opinions in the development and governance stages, the risk of building biased AI goes up. And this only perpetuates the societal inequalities and exclusions that we should all be seeking to tackle. Building in fairness through diversity and inclusion is absolutely critical part of building good, responsible AI.
What role can leaders play in challenging gender inequality?
I think there is a responsibility on all female leaders, not just to be figureheads – though that’s important – but also personally. Mentoring and sponsorship is very powerful – it can genuinely change lives. In a one-to-one context you can encourage potential female leaders and work through some of the challenges they’re facing, often being reminded in the process of how you’ve struggled (are still likely struggling) against some of these confidence and glass ceiling issues. Speaking up on the topic, talking about how it was for us and saying to other women: “No, you’re not crazy, these biases do exist and it’s not easy”.
I have had some phenomenal female mentoring in my career and I try to pass on as much of that wisdom as I can. One of them told me not just to aim high, but to make sure that the more responsibility I achieved, the more I used that position of influence to make it easier for other women and be “noisy about it”, which I thought was great advice.
Intersectionality is also very important. It’s absolutely right that we talk about women, and about other minority and excluded groups. But there are also, of course, women who are people of colour, women who are part of the disabled community, and so on. Intersectionality means they face particular difficulties. It’s important not to forget that there are people facing several of these potentially exclusionary factors and we can’t lump all women together.
What advice do you have for data and AI professionals striving for leadership positions?
You should be thinking hard about how, as leaders, you can ensure innovation is always done purposefully, responsibly, and ethically. It’s not enough to think “could we do this?” you need to be thinking “should we?”
When it comes to accountability, the buck stops at management. So be thinking about how you can use your role to promote good practice, and keep the people whose data is being used front and centre in everything. How can you put in place the right governance structures and embed a strong ethical mind-set across your organisation?
A big part of this is making sure you have diverse teams in place and an inclusive environment for people from all backgrounds and walks of life. It’s going to make your business a much better place to work not just culturally but in the quality of the AI and other data products you build.