Interview: Dr Zara Nanu, CEO, Gapsquare
Image credit: Nick Smith
An AI-based software program has been developed to help organisations tackle gender pay inequality in the pursuit of a fairer workplace. Social entrepreneur and CEO of Gapsquare Zara Nanu MBE explains why.
When the World Economic Forum announced in 2015 that “we are 217 years away from closing the gender pay gap”, Dr Zara Nanu decided that something needed to be done about it. At the same conference, she heard that by 2030 most of us will be in self-driving cars. “I also heard that Nasa was saying we’ll be waving people off to Mars.” The CEO of Gapsquare says it was hard to listen to these short-range technology predictions while realising “we’re still two centuries away from pay parity”.
Gapsquare develops AI-based software that crunches employment data to address issues such as gender pay inequality, with Nanu seeing it as her mission “to help organisations put a stop to biases and prejudicial practices preventing equality”. In recognition of her achievements, Nanu received an MBE in the late Queen Elizabeth’s Jubilee Birthday Honours list, for “services to tackling global workplace inequalities and promoting fairness and inclusion”.
These predictions about our technological pathway in space exploration and personal transportation only served to focus her attention on the gender pay gap. She says it led her to wonder if “we could leverage the data in tech and innovation to accelerate pay equality in work, so that we could look at that disparity in why men and women are still paid differently”. She reflects on her early ideas on the issue: “Perhaps I was a little naïve in thinking that AI could solve everything.”
The 42-year-old entrepreneur quickly came to realise that punching masses of raw historical data into an algorithm won’t solve everything, because humans are biased, meaning that “unleashing data from payroll and HR” would not uncover “where the gaps were and how to address them. I thought that if we did that we’d solve the problem. But I couldn’t have been further from the truth because what you put in you get out.”
Nanu says she looked at companies with payroll data on hundreds of thousands of employees: “What the algorithm was learning was that men should be paid more than women, making predictions and recommendations saying that men should be in senior roles. This is because it was based on historical data. In the quest to collect data to promote pay equality, we had to be really careful, because it’s not straightforward.”
Back to space exploration. An often-used colloquialism expressing disappointment in the failure to progress is: “We can put men on the Moon, but we still can’t do xyz.” And yet, comparing a conspicuous success with a trivial and commonplace failure is relevant to Nanu’s point in that, if you look at the statistics regarding astronauts that have set foot on the lunar surface, you immediately see the limitations of historical viewpoints. Twelve white men. Referring to this data set, Nanu observes: “Trends are helpful, because they allow us to see what is going on in an organisation. When you bring them together with your business objectives, you can start to produce more meaningful insights about the future.” Nasa has recently stated that its current robotic and human Moon exploration mission programme, Artemis, “will land the first woman and first person of colour on the Moon.”
The idea of bias Nanu says once organisations become aware of what their data means “it’s easier to start to think objectively about where you are and where you want to be, how to make it happen and what indicators to track”. She adds that while just under half of gender pay difference can be explained by factors such as experience and qualifications, the remainder is harder to understand without the idea of bias: “We also see occupational segregation where the pay is higher in fields dominated by men than those dominated by women. For instance in the tech sector, pay is much higher than in nursing.”
One of the reasons that analysis of historical data is important is that it opens a window on “the way we value jobs, which is still not very structured”. In fact, Nanu continues, the general perception of gender in employment is still based on attitudes that emerged in the mid-20th century. “What we think about jobs in health, care, engineering and so on, was decided in a completely different world to the one we live in now.”
A more structured approach, Nanu says, would be to examine what tasks are involved in a job in order to discover which are similar, “so that we can assign similar value to them”. She then refers to a current trend in employment tribunals related to the discrepancy in gender pay in retail between front-of-house jobs such as checkout, and back-of-house duties in the warehouse. Data reveals that “people at check-out are predominantly women, and in the warehouse men. The women, who were being paid less, took their cases to tribunals arguing that they brought the same value to the business.” The arguments around why warehouse jobs are paid more, says Nanu, “no longer stand because those jobs have evolved. If there was an argument that warehouse work is harder because it is more manual, that is not necessarily true now because a lot of the processes have been automated. Looking at data to understand outputs and tasks allows us to come to a closer value of the work.”
At the core of Bristol-based Gapsquare, acquired by XpertHR in 2021, is its cloud-based software designed to “bring together payroll and HR data so organisations can understand pay gaps and how they value their employees across gender, race, ethnicity, disability and other employment characteristics at different levels of detail. There’s also an element for understanding trends as well as workforce planning. This allows organisations to develop a diverse ecosystem to support talent through the pipeline to higher levels of the organisation. We know from research by organisations such as McKinsey and Harvard how important diversity is, and we know how detrimental lack of diversity can be, because you end up only producing products and services that fit a specific demographic.
“You want to make sure that products such as smartwatches, for example, can be used by men and women, left-handed and right-handed people and so on.” The program is intended for larger organisations (“usually 250 people or more”) and Nanu, who is also chair of the Women in Business Task Group, explains that included in the 100-plus organisations using Gapsquare are Condé Nast, Accenture, London’s Metropolitan Police and the London Mayor’s office.
Nanu also thinks data can remove assumptions based on entrenched anecdotal and circumstantial ideas. “One of the things that we’ve definitely noticed over the past five years,” she says, is how much such assumptions “impact the emotional and political side of diversity and inequality in an organisation. This is because you have people saying: ‘oh, it’s all a big myth. Everyone’s paid equally. We have a meritocracy.’ But then when they sit down and look at the numbers, they speak for themselves.” Which means, “more companies now are setting targets for what diversity should look like in their C-suite. To make that happen, they need to grow an entire pipeline. Keeping track of data, understanding trends and making predictions across an organisation helps organisations get there.”
‘The way we value jobs is still not very structured’
Getting there means complying with legislation that is already in place in the UK and Europe, “and that requires showing your figures”, says Nanu. This became more difficult as work changed during the global pandemic. Not only did workers reassess their own values during the ‘Great Resignation’, but equality of pay became harder to evaluate as workers spent more time in remote tech-enabled contexts.
“Jobs are constantly changing,” says Nanu, “and Covid-19 has played a role in accelerating these changes. On one hand, we have automation and AI reshaping how we do our work, and on the other hand, we have people working in hybrid settings, picking up skills or decision-making tasks that might not be so visible to the employer when they work from home.”
Born in Moldova and fluent in several languages, Nanu says that there’s not much in her secondary education that pointed towards her following a career in AI and statistical regression models. “I would have laughed, because maths was ‘not for girls’: at least in Moldova, where I grew up. Girls were doing languages and literature and what would have been considered at the time ‘softer’ skills.” Despite which, she studied history and education at the Pedagogical State University in Chisinau, while focusing on policy and human rights. During these intellectually formative years, “the more I heard the phrase ‘women’s empowerment’, the more I heard the word ‘power’. Power means money and money means maths. Most of the important decisions that are made around the world are based on maths, and not a lot of women are part of that world.”
Growing up in a former Soviet socialist republic (that is currently a candidate for accession to the European Union) and coming from a family with generations of teachers, Nanu became interested in the gender pay gap after working with a female trafficking prevention programme in Moldova, where she discovered that women were working in sweatshops on minimum wage with no clear route out of the poverty trap. Feeling the need to become more involved, Nanu worked in the women’s empowerment space “for quite a few years, but then wanted to do something centred on money and tech”.
A master’s degree in public administration and international management was followed by a PhD in political science at the University of the West of England. Armed with her doctorate, Nanu then stood for parliament in Bristol as a Green Party candidate, securing “all of one thousand and something votes. It’s a very safe Conservative constituency.” During a stint working at the Young Women’s Trust, Nanu realised that she was seeing “more and more inequity and I wanted to do something about it. That’s how I started to move things forward with Gapsquare.” Reflecting on her initial impressions of the UK, she says that in Moldova, “we had quotas around women in parliament, quotas around the representation of women in any sector. Childcare was free, so my mum could go back to work six months after giving birth. When I came to the UK it felt like I was going back in time in terms of gender equality.”
The problem for Nanu was that all the talk was about raising awareness, which meant her entrepreneurial instincts were hardly encouraged by what she saw as “a lot of projects – not many of which were actually generating more wealth for women. It’s hard to take decisions in life when your first thought is ‘what are my children going to eat today?’.” She identified two possible routes. The first option was to go into consultancy “and work with large businesses to create more equality within them and more equal pay”. One the other hand, “technology was also offering a solution”. This second option resonated with her, “because you don’t have to be a huge operation to develop software that can be used globally”. As Nanu says, it’s a simple fact that at some point “to do something” you have to stop pursuing awareness and raise money.
In 2020 the World Economic Forum published its ‘The Future of Jobs’ report, part of which analyses demand trends for types of occupation. On the increase were jobs in artificial intelligence, machine learning, data science, robotics, Internet of Things and so on, while there was a forecast decrease in demand areas such as mechanics, manufacturing assembly, accountancy, administration, and data entry. “Once you start to apply the gender dimension to these jobs,” says Nanu, “you see that most of the occupations that are currently projected to increase in demand are male dominated,” while many of the lower-paid jobs that are on the way out tend to be done by women. For Nanu, the report backs up the idea that the “preconceptions of what the world of work looks like now” remain similar to how they were in the post-World War Two era.
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