Closing the Gender Pay Gap using AI – an interview with Dr Zara Nanu

Host: Marco Oliver, Client Success Director at We Build Bots

Guest: Dr Zara Nanu, CEO at Gapsquare

Marco Oliver: Hi everyone, and welcome back to TheBotcast episode 2. That’s right, we’ve made a past episode 1 and I think we’re really starting to find our stride on the whole podcast thing now and really enjoying it. So, if you haven’t already, please go and check out episode 1 with Julian Harris from CognitionX. We talked to him about all things chatbot, how chatbots are at the moment and how they could be advanced in the future.

He’s super intelligent and he makes for a really interesting interviewee. He sets a really good foundation for what we’re trying to achieve with the podcast and it means that we can now start to drill down into the detail and focus on specific sectors and business areas across the UK and hopefully worldwide.

In order to do that we’re going to be bringing you some quite exciting guests, and one of those is on today. We have Dr Zara Nanu, CEO of Gapsquare on the Botcast! Gapsquare is a tech company using AI to close the gender pay gap. The plan for Gapsquare is to reduce the gender pay gap within 20 years, which some people might say is very optimistic, especially considering that The World Economic Forum have said that it’s going to take 217 years - I will ask Zara about that. Having spoken to Zara and understood her passion for women’s rights and what drives her, I know that she’s building a fantastic team and if anybody’s going to be able to do it, I think it will be Gapsquare. So, Zara was a pleasure to interview and we should just get straight to it.

Hi Zara. Thank you for joining us today. How are you?

Zara Nanu: Hi. Yeah, I’m very good. Thank you very much.

MO: Good. So, would you be able to give our listeners an insight into your work history and how you became CEO of Gapsquare?

ZN: Yeah. So, it’s a very interesting story. I think it goes back to when I was in school. If anyone when I was in school would have told me that I was going to be running a tech company I would have laughed in their face, because my favourite three words at that time were I hate maths. And now I use maths as statistics to help companies understand the gender pay gap. So, at that point in time when I used to hate maths, my path in life took me more into the charity sector and into the public sector and I worked a lot around women’s rights and human rights.

I worked for an anti-trafficking charity in Moldova. We were working on preventing trafficking of women by placing them in different employment opportunities or by providing them with livelihood skills so that they wouldn’t be trafficked into Europe. I worked for a charity called Young Women’s Trust managing a women’s centre in South Bristol, working with women who were leaving the criminal justice system and women with anti-natal post-natal depression. I was doing all that work for over a decade and the thing that stood out for me most is how the charity sector does do a lot of work around women’s rights however it doesn’t necessarily always transcend into the business sector.

So, you have businesses that are working but not necessarily incorporating a lot of those values and a lot of those issues around women and women’s access to the economy. So, that’s when I thought maybe it’s time to make a transition to the private sector and bring a lot of those values with me here, and that’s how I set up Gapsquare about two and a half years ago.

MO: Wow. That’s really impressive actually. So, you’ve had quite a heavy focus on women’s rights throughout your career. Where did that passion really stem from?

The World Economic Forum have said that it’s going to take 217 years to close the gender pay gap. Dr Zara Nanu and her team are aiming to do this in under 20.

ZN: I originally come from Moldova; a country that was part of the Soviet Union and there was, at least on face value, some gender equality. Women had access to free childcare where they could leave their children and return back to work. There were a lot of things that facilitated women’s access back into employment. And as I started going around the world - I spent a couple of years in the States and then I came to the UK, to me, it felt like we’re looking at societies that are almost behind in terms of women’s access to the economy and women’s rights. And so that’s how I got more involved in this, which is really interesting because usually it’s the other way around. It’s people going to countries like Moldova.

MO: Yeah, exactly. I think that’s a misconception sometimes because I think in the UK we tend to think we’re always ahead of the curve on that kind of thing. But as you’re saying Moldova, for example, are much further ahead of us.

So, the World Economic Forum said that they anticipated taking 217 years to close the gender pay gap, whereas at Gapsquare you’re going to try and do that in under 20 which is mightily impressive. Can you give us an insight into how the business works and how you’re planning on achieving such an amazing goal?

ZN: Yeah. I mean, the short answer is technology. The longer answer is that the same World Economic Forum is saying that by 2030 most of us will be in self-driving cars, hospitals will be on their way out because a lot of the healthcare will be happening at home and will be automated, plus we will have less accidents because of the self-driving cars. We will be on our way to the Red Planet and yet we will be, like they said, 200 years away from actually closing the gender pay gap.

We’re looking at leveraging the same technology that will facilitate us going on to Mars and creating self-driving cars and using it to advance gender equality. Not to mean that we’re going to build a rocket and send women to a place where there is gender equality. But what we do is we use artificial intelligence and combine that with companies’ data around pay and HR to help them understand where the gaps are and take data driven decisions to narrow those gaps much faster.

We’re looking at leveraging the same technology that will facilitate us going on to Mars and creating self-driving cars and using it to advance gender equality.

MP: That’s a really interesting point actually. So, what you said there, is that the AI is essentially allowing you to reduce your timeframes by completing a lot of the analysis and processing of data for you, which it might have taken experts several weeks before, and you can probably process that in hours. That must be having a massive impact on your business.

ZN: Yes. And it’s interesting because it’s the same AI that can actually, if we don’t use it carefully, push it the other way round because we live in a quite biased society where there’s a lot of assumptions about people’s roles, what they should be doing and occupations.

I was talking, for instance, to some 7-year-olds a while ago about the gender pay gap and at the age of 7 they were already saying that women shouldn’t be working in construction and they should be staying home taking care of babies and men should be going to work. A lot of these biases that we have in us, and we see even in young children, we can then embed into artificial intelligence. So, we want to make sure that the technology we develop is aware of these biases and removes them in looking at data.

To date we have collected data for over 200,000 employees in the UK alone and we do see these biases in every single data set.

MO: Yeah. So, I suppose the main risk really there is that we’re passing the data into the AI and the machine learning capabilities that you’re utilising. But in doing so we’re potentially introducing the biases of the past through, for example, if we took information or data from an HR system and that had previous hiring or firing data held within that then we could be introducing those biases of the past, when really we’re trying to introduce improvements for the future. So, how are you handling that and preventing the data from affecting the work that you’re trying to do?

ZN: To date we have collected data for over 200,000 employees in the UK alone and we do see these biases in every single data set. They’re already there. They are what creates the gender pay gap which in the UK right now is at around 18%. But the algorithms that we develop bring together also a lot of research and analysis around gender equality and diversity to bypass these biases. And we take into account innovative research from people like Iris Bohnet, who’s the behavioural economist at Harvard, and others to embed those in the way our AI understands that data and then makes recommendations for companies based on that data.

MO: So, you’re constantly improving the artificial intelligence and tweaking the data and that’s great. I mean, it’s an evolutionary process, isn’t it? So, by now you must have quite a well-known presence in that space and people must be coming to you and saying Gapsquare, can you help me understand the gender pay gap in my company and how I can improve the gender pay gap? So, could you maybe give us an understanding of how the customer journey works and what the approach would be if a company came to you?

ZN: We are a cloud-based software and companies sign up to have a licence and then they run their data through our software on a regular basis. We feel that it’s important that we develop this technology in this way for two reasons. 1) We want to put data and the analysis back in the hands of the decision makers because usually equality and diversity is passed on to consultants - that’s good because you have consultants coming in with expertise but they’re there only for a limited time and they don’t necessarily understand the culture of the organisation, the historical background, and the decision-making process within the organisation. So, we want to put that analysis and the data insights back into the hands of the decision makers.

Also doing this kind of work regularly and embedding this analysis in the operations of the company can ensure that the gender pay gap continues to decrease. We want to make sure companies do this regularly, like, once every month or quarterly, not just an exercise that the do once a year and then tick a box and move on because that’s not going to generate meaningful insights.

MO: So, it’s about using the data to be proactive rather than reactive, isn’t it? You don’t want to get to to the end of the year and then just turn around and say oh, we’ve not had a great year this year. The gap has widened. We need to do something about it. It’s about on a monthly basis making changes and small tweaks.

ZN: Absolutely. Because we’ve had companies now who’ve used our software for two years and they see the benefit of using it regularly. Because by the time they analyse it in a year, it’s already too late to take any decisions because you get to a point where, hang on a second, the gender pay gap is the same as it was last year, nothing has changed. So, you can’t leave it for a full year. It’s important to do that more regularly and look at the data insights more regularly. Also our AI technology is able to then pull more insights and understand more trends the more regularly the data is uploaded.

There was a recent Gartner report that showed that for companies with 10,000 employees it would, roughly speaking, take about 14 million pounds to close the gender pay gap if they wanted to do it now.

MO: Wow.

ZN: And for every year that they don’t look at this issue they accrue another 250,000 pounds that they’re going to have to spend at one point. We live in a world where employees are more and more interested in pay and how much other people are paid. Fair pay is definitely an important issue for millennials, and they’re very open about how much they get paid and they want women to be paid the same as men.

A recent Gartner report showed that for companies with 10,000 employees it would, roughly speaking, take about 14 million pounds to close the gender pay gap if they wanted to do it now and for every year that they don’t look at this issue they accrue another 250,000 pounds that they’re going to have to spend at one point.

MO: Yeah. And rightly so. I think the deterioration rates you’ve mentioned there are much larger than I was expecting actually. So, what companies would you say at the moment are using this technology and AI to really improve their gender pay gap? You can maybe use some of the examples of people that you’ve been working with.

ZN: I’m pleased to say that a lot of companies are taking this issue seriously. Already by using our software they’re committed to making change and tracking that change and monitoring it because what gets measured then gets done.

A lot of the time things around equality and diversity can become very political in a company and therefore get side-lined. But when you tie these issues in with data then they become a management issue and it’s much easier to figure it out. I’d have to say public sector companies, I think, are taking this issue seriously because they’ve had an equality duty for a much longer time. So, they’ve been looking at this for about a decade. Private companies are catching up quite quickly. We have clients like Vodaphone who are spearheading the way around gender equality and diversity in their company and many others doing the same.

MO: Yeah. I’d have to agree with you there having worked in both the public and the private sector. I started in the public sector about 13 years ago and even as far back as that, the first thing they would do is they’d put you on to a diversity course.

I think the private sector is definitely catching up with the public sector, especially in IT and technology. My personal experience is I found that in positions of power within the IT department, especially in the private sector, they’re quite male orientated and dominated whereas in the public sector there’s definitely a gender equality and more of a balance, I think. So, you do find a lot more women in managerial roles and heads of department and so on. Is that the same kind of thing that you’re finding from the data you’re pulling out of the artificial intelligence and analytics it’s generating?

ZN: Yes, absolutely. And one interesting fact is that Greater London Authority, London Mayor’s Office, was one of our first clients and they’ve used the software for about two years and now they use it to also monitor ethnicity pay gap. It’s interesting for us because Patrick, who has been using the software on there, has now joined our team on secondment for 12 months. So, it’s really great to see that bringing those experiences together, but also it validates the fact that we’re doing the right thing. When someone who’s a client wants to come and join your team, that’s when you know that you’re doing the right thing.

MO: Exactly. And that must be great to get a validation almost and a buy-in from someone that you’re working with and they believe that much in what you’re doing that they’ve jumped ship and come over to you, that’s fantastic. So, that’s probably a really good point to end there, Zara. Is there anything you wanted to summarise and close the podcast on?

ZN: No. I think the key thing is that we will be pushing to be closing the gender pay gap in the next 20 years. And the more companies that use our software the faster we’ll be able to do it because we can pull all of those data insights together and actually focus decision-making on where it matters.

Some companies can be spending money on, for instance, mentoring schemes, sponsorship schemes or training, when actually, by looking at data they can see that all they need is a little salary adjustment and then the problem will be fixed and they will spend much less money on it. It’s about focusing and using technology the way we started using it to advance many other things in our lives.

MO: I couldn’t agree more, to be honest, Zara. Thank you ever so much for agreeing to be a guest on the show. You’ve been fantastic to interview. And before you do go, we ask every guest the same question, and that is, if you could only live with one app for the rest of your life, what app would you choose? You’re not allowed any other apps.

ZN: That is the most difficult question. My to do list. I think I’d be lost without it. I have an app, Wunderlist, where I keep the list of things that I have to do. I think I’ve got to go with that one.

MO: I would agree with you there. It does give a bit of structure to your day. Thanks Zara!

ZN: Great. Thank you Marco.

MO: Well, that was the interview with Dr Zara Nanu. If you do have any feedback, then please do message us. You can use the Anchor app or you can contact us with #thebotcast on any of your favourite social media platforms. You can also use both #thebotcast and Anchor to send us suggestions and questions for future guests, and any topics that you might want covered, so please do that as well. Be sure to subscribe via any of your favourite podcast providers if you want to be kept up to date and notified when we put another episode live.