Women in tech report: on equity, opportunity, and representation in Egypt
Updated: Mar 29, 2022
Young, Wajcman, and Spreijer (2021)
In light of Women’s History Month, Aliah Yacoub and Youmna Hashem pen this exploratory report on women’s space in the field of tech and artificial intelligence (AI) in Egypt.
This report is divided into four key sections: section 1 offers a theoretical grounding into gendered epistemology and data feminism; section 2 looks outwardly, diving into some of the statistics surrounding gender inequity in Egypt's tech scene; section 3 looks inwards, providing a glimpse into Synapse Analytics’ own employee statistics, practices, and mitigation efforts; section 4 looks forward, outlining pathways and actionable steps for a future of gender-equitable tech innovation.
Gendered epistemology and data feminism: a primer
Research has shown that in the tech world, women are grossly underrepresented in every stage of production: from the theoretical to the technical. This is more pronounced in AI, where gender inequality has far-reaching implications and very real, possibly dangerous repercussions.
AI is the supposed actualization and replication of human intelligence, but the very theory of ‘intelligence’ and the epistemology operationalized by dominant AI research has been focusing only on a specific form of knowing—a male form. Work in feminist epistemology has shown that gender affects our acquisition and practice of ‘knowledge’ and that focusing on ‘male intelligence’ in AI effectively excludes female epistemologies.
The reification of gendered and racialized conceptualizations of ‘intelligence’ means that your Siri or Alexa, although feminized in name, would only think like a man –as if it’s the default epistemology.
The theoretical and socio-technical exclusion of women’s knowledge is exacerbated by their underrepresentation in the tech workforce - on average, women represent less than 20% of STEM occupants globally . This is a problem for your dataset, your business, and society at large.
Many counter-movements have emerged that attempt to identify and tackle these structural inequalities. The field of data feminism is one such movement, providing us with an invaluable, intersectional blueprint for addressing inequity in tech.
At its core is the belief that contemporary injustices - such as gender inequity, racial profiling, and class segregation - are all deeply enmeshed “in historical and contemporary differentials of power”, and that a passive approach to technology and its development reproduces these inequalities into our modern technologies and, by extension, societies. Data feminism attempts to highlight and break down these structures to avoid their replication in the digital sphere.
This approach to technology is therefore twofold: firstly, to deconstruct the many ways in which current practices in the fields of data science and AI are mirroring and reproducing social and gendered inequalities; secondly, to utilize data itself to highlight, disrupt, and challenge these inequalities.
Beyond the social, if the technical products seem to be working fine, why does representation matter so much? Should it really matter who is developing the technology? The short and long answer is: it absolutely matters. Put simply, there is immense strength and necessity in diversity. Consider a scenario in which an AI company exclusively hired data and research scientists, forgoing software and machine learning engineers. The data - collected, analyzed, and interpreted - would sit idle without the right expertise to operationalize it.
In much the same way individuals with different skill sets provide unique value to companies, so too do individuals from different social, economic, and racial backgrounds.
Further research has found that a diverse workforce actually increases innovation and productivity, in turn boosting profit. The ramifications of inequitable gender representation in the field of tech extends beyond lost economic opportunities for women: it impacts innovation, the way products are designed, the purposes they serve, and - more importantly - who they serve.
The diversity problem therefore reflects, and often even amplifies, structural inequalities present in society. This creates what researchers refer to as the “discriminatory feedback loop,” where gender inequity in the workplace results in inequitable access to resources, the development of inequitable tools, and ultimately the creation of discriminatory products that benefit those they were built by and for.
So where do we begin? Well, with ourselves. As with tackling any challenge, a clear understanding of its parameters is key. This is where data comes in. In the following section, we’ll provide an overview of the general state of gender inequity in Egypt's tech landscape before reflecting on our own statistics and outlining the steps we plan on taking.
Statistics from Egypt
The data feminist approach to mitigating gendered inequalities in tech necessitates tracing biases in the field of technology back to their source. In the case of women’s participation in the tech workforce, doing so provides us with some interesting insights .
Over the past few decades, Egypt has made significant progress in its efforts to close the gender gap in education. According to CAPMAS data, women made up 52% of the total number of students enrolled in both public and private universities in 2019 . Of the 507,000 students enrolled in STEM disciplines that same year, women accounted for 47.3% .
Data from a 2014 census indicates that 48% of Masters graduates and 39% of PhD graduates in Egypt were female - given the trajectory of undergraduate statistics, that number is only likely to have increased over the past 8 years) .
Despite the more equitable representation of women in STEM education - and despite women in the field being highly qualified - these statistics do not carry over into the workforce. As of 2020, women make up only 38% of those employed in STEM . That same year, women made up only 16.13% of the employable Egyptian labor force - the second lowest recorded number over the past decade .
Socio-cultural norms play a pivotal part in sustaining gendered roles in the labor market, effectively impacting women’s equitable participation in the labor force more generally. Social understandings of ‘masculine’ vs ‘feminine’ labor, as well as perceptions of women as primarily caretakers, create rigid stereotypes in the workplace. These stereotypes create deeply embedded unconscious biases and discriminatory employment practices in private sector positions in STEM - all of which can be incredibly difficult to overcome .
Further research has found that - given societal expectations - women in Egypt have a clear preference for public sector jobs, as these offer more flexibility and job security as opposed to the private sector . This includes jobs in academia and scientific research .
Mounting research has shown that women have also been more disproportionately impacted by job losses and financial exclusion in the wake of the pandemic . This is especially significant: the pandemic has exacerbated existent inequalities and heightened the need for remedial solutions.
Synapse Analytics' statistics
While the structures that inform these inequalities are tacit and oftentimes out of our control, mitigating their effects is well within our reach. There is a pressing need for transparency surrounding diversity statistics in tech companies - a transparency that can only be achieved if companies are willing to publish their statistics.
As a data science and AI company committed to developing accessible technology and digital solutions, we recognise our role in contributing to an equitable digital future for our region.
As such, we conducted an in-depth analysis of our applicant database, disaggregated by gender. Below, we outline some of the key findings from this self-reflexive exercise, before delving into changes we plan on implementing to help mitigate the gender gap in our own workforce.
Our database contains information on applicants to the following positions: Backend Engineer, Frontend Engineer, Senior Frontend Engineer, DevOps Engineer, Product Management, UI/UX, and Graphic Designer. The timeframe for these applications was between August 2020 and February 2022.
Altogether, we received 615 applications across the listed positions. At 209 out of 615, women represented 33% of applicants (Fig 1). This number adds an interesting dimension to our previous research findings, as it affirms the view that - once again, despite their equitable representation in education - women are more hesitant to apply to private sector jobs in STEM.
A closer look at the gender breakdown of each of the positions reveals more meaningful insights (Fig. 2). Across the listed positions, males made up the majority of applicants by more than double - with the exception of Graphic Design and UI/UX.
Here, we can see a clear instance of the aforementioned gendering of labor. Perceived as more social, artistic, and creative fields, graphic design and UI/UX jobs are more often associated with women than with men. Conversely, DevOps and Backend Engineer - seen as more ‘scientific’ and ‘technical’ - were more saturated with male applicants.
The rate at which women are applying to positions at Synapse translates into low female representation in our workforce. Altogether, the company has 41 employees. 9 of these 41 employees are female, representing 22% of our overall team. The gender imparity within our team mirrors - to an extent - the gender imparity within applications, and - more broadly - that of inequitable representation beyond the gates of academia.
Synapse Analytics' strategy
Although transparency and self-awareness on any entity’s part are important, there is a pressing need to complement that awareness with action. We can only be true to our theory if we translate it into praxis; in what follows, we outline the actions our company has taken (and will be taking) to ensure our commitment to gender-equitable tech innovation.
The very creation of techQualia, Synapse’s in-house publication, is a testament to this commitment. Led by two female social scientists, techQualia is in essence a self-reflexive machine. It stands on two axes: the first is inwardly directed, wherein it’s occasionally consulted on the company’s tech services and products in terms of algorithmic biases and data privacy to ensure they are fair and accessible; the second is outwardly directed, with techQualia publishing weekly content that is heavily critical, informative, and free, to localize knowledge about AI and its wider socio-economic repercussions.
Synapse Analytics is also taking more steps towards establishing itself as an open and inclusive work environment. The steps range from providing a space for women’s voices to be heard, to having a woman sit in on technical interviews, and to constantly and consistently updating our database of applicants to make sure the numbers reflect the efforts taken.
For this reason, we have begun collecting sex disaggregated data from applicants. This allows us to be more mindful of our hiring practices and act responsibly.
The gender gap in the MENA region’s tech scene is still considerably large and there has been an appallingly minuscule amount of data collected that can capture its extent and help inform mitigation efforts. While researching, we found a lack of accessible, nation-wide, reiterative open data that we can use. This is an issue that hinders gender mainstreaming for any company, which would otherwise be forced to operate based on mere guesses.
Although the numbers in the statistics we’ve highlighted earlier reflect gender equality in education and a linear progression when it comes to the number of women in STEM, it is not translated into full integration into the tech workforce, with women only making up only 38% of those employed in STEM in Egypt.
To bridge the gap between the number of women in STEM education and the number of women employed in tech, more effort should be put into breaking down the structural (be it societal, economic, or political) barriers that prevent or hinder women’s integration.
In order to do that, the private sector - where there’s a lower number of women applying due to higher recorded instances of gendered discrimination in hiring and inflexible working conditions  - and tech companies more generally, must employ a more critical, self-reflexive approach and arm themselves with sex disaggregated data that can inform their policies.
Not only is the playing field not leveled for female employment, it is also uneven for investment in female entrepreneurs. A WAMDA report showed that - out of $2.8 billion raised in funds last year in the MENA - only 1.2% of these investments were directed at female-founded startups .
While the social, economic, and cultural barriers to gender equity in the field are incredibly complex and might seem largely beyond any one company’s control, as employers in the field, we also recognise the small (but meaningful) role we can play in helping lower women’s barriers to entry into STEM.
Young, E., Wajcman, J. and Sprejer, L. (2021). Where are the Women? Mapping the Gender Job Gap in AI. The Alan Turing Institute. Retrieved from https://www.turing.ac.uk/research/publications/report-where-are-women-mapping-gender-job-gap-ai
 ILO (2020). How many women work in STEM? International Labour Organization. Retrieved from https://ilostat.ilo.org/how-many-women-work-in-stem/