Linda Zwane, Head of Data Management at Standard Bank Eswatini, unpacks her experience in the tech space and how the gender bias compromises the sector.
It is not uncommon to hear criticisms of the tech sector for a significant lack of gender diversity at all levels, but specifically within decision-making positions.
According to the World Economic Forum’s latest Global Gender Gap report, women make up less than 40% of the total workforce in the top tech companies, and when considering only tech-related roles they are even more underrepresented.
This comes at a time when accelerated advances in tech fields like artificial intelligence are having a profound impact on the economy and society at large. This is only set to deepen due to the acceleration of technological advancements as a result of the impact of the Covid-19 pandemic.
But while AI applications hold promise to transform our lives in many aspects, they also present serious risks. A narrow talent pool in AI could perpetuate existing forms of structural gender inequality.
It should take talent and hard work in an individual to have a successful career in Data and AI, as in any industry. But certainly, in my experience, it has required more of me. From the time I was interested in mathematics as a younger person, it made me different.
In university lecture halls, the number of women got leaner as we neared graduation. In a postgraduate study, there was also the isolation of being the only person of colour in all of my chosen classes, over and above the gender divide highlighted by the quoted WEF study.
The practical impact of this lack of diversity, is that women in university lecture halls either work in groups consisting largely of men or get isolated from the collaborative culture that the coding world sometimes requires.
It took exposure to the one African and female lecturer I had in the entire course of my undergrad program to show me it was possible and that there was room in the world for some who looked like me and had similar interests to myself.
Gartner predicts that by 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data, algorithms, or the teams responsible for managing them.
Already we see examples of this in the form of virtual assistants and chatbots that make sexist assumptions while there are instances of targeted algorithms that are perpetuating the pay gap by gearing better-paid jobs towards men.
Taking into consideration that only 22% of AI professionals globally are female compared to the 78% who are male, as stated in the WEF report, the picture is worrying. In South Africa, on average 28% of the AI talent pool is female in contrast to 72% male. Across the rest of Africa, these numbers are expected to be even lower.
The impact of this bias in AI is well-documented.
Individuals are profiled with unfavorable consequences, access to important services is compromised. And all the gaps widen the gap between exposure to the profession and the people it seeks to attract, as the image of participation and leadership in our sector is maintained rather than transformed to be more inclusive. Bias accelerates the divide. At best, the status quo is maintained. At worse, inclusivity becomes even more elusive. It is an ethical question of what comprises the right data; over and above what is necessary and sufficient.
This emphasises the urgent need to take action that incorporates awareness, improved education, transformation, inclusivity, and enablement for females.
An increase in AI technology
As companies race to expand their digital capabilities by leveraging disruptive technologies like AI, cloud computing, big data, and others to remain competitive and relevant in the current environment, there will be an ever-greater demand for technical skills and innovation capacity.
This is especially true for the financial services industry, where significant investments are being made to enhance customer experience and engagement through new digital capabilities. Billions are being spent on catching our continent up to world standards in data and AI, necessarily so.
AI is an area of development in which self-advocacy is critical. With the rapid change in the pace of development, with a race to reach the unbanked, and with several women’s propositions being created, recognizing the pivotal role women make as decision-makers in homes across the continent, it is evident that unless these propositions speak to the end-user, these massive investments may limit the very value they seek to create, by design.
For more women to enter this talent pool requires us to break down the opposition that so many women experience. The WEF report shows that there is still a perception among 52% of women that technology is a male industry, while almost a third (32%) believe gender bias is still a major hurdle in the recruitment process.
Commitment to developing women in tech
Standard Bank recognizes this and has several Women’s Leadership Development programmes, that invest significantly in the growth and development of women in the organization.
As we enter the new wave of industrialisation, we must do better as an industry to remove unconscious bias to create a more inclusive environment. The most effective route to success is when teams are diverse in their representation and this starts at the top.
We need to open dialogue around the pervasive stereotypes around gender.
At Standard Bank, we have been on our own journey to promote inclusion and gender equality both internally and in the wider financial services ecosystem. We participate in programmes and drive initiatives designed to provide both women and men the opportunity to enhance their capabilities in skills of the future such as data science and analytics.
Standard Bank’s Data Science Mastery programme, launched in 2016, provides practical experience to greenfield graduates and experienced staff members, who are fused together and exposed to different areas of the business where unique problems exist. The mentorship aspect of the programme, which is diverse in nature, plays an instrumental role in establishing a diverse talent pool within the organisation.
The structural bias we see in the Data and AI profession currently is a reflection of the structural biases that exist in society. To shift them, we shift society. To shift society, we expose all young people as widely as possible, and foster environments that they can grow their potential and their curiosity.
The world is facing a digital skills gap and gender plays little role in this regard. As men and women, we must empower ourselves with the skills needed to stay relevant in the future of work. In this era, the onus is on everyone to pursue education and keep abreast of evolving trends.
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