By Mia Hem
The digital revolution has immense potential for the improvement of the social and economic position of women in society. Yet, it can also lead to significant risks of perpetuating existing patterns of gender inequality. Despite many efforts being made to address these inequalities, a gender gap remains, limiting the equitable realization of the benefits of digital transformation across high, low, and middle-income countries.
With a persisting gender gap in ICTs, women are underrepresented in the digital revolution, which leads to further inequalities. To address these inequalities, more attention needs to be paid to social, political, and economic factors that lead to the development, design, and use of digital technologies. Digital technologies are shaped by gender relations and gendered power structures. Systemic risks (and opportunities) exist, and specific educational, technological, and policy solutions would mitigate these problems. There are three main areas to focus on to identify opportunities and risks in the digital revolution: education, work, and social/welfare services.
Since the Beijing Declaration and Platform for Action, digital technologies have rapidly spread and developed across almost all aspects of socio-political and economic life. It is evident in public and private industries, including health care, commerce, education, manufacturing, and finance, and has brought with it a new digital economy across developed and developing economies alike. AI has become a defining feature and driving force of this data-driven, digital revolution. This digitalization also leads to utopian and dystopian visions of the future, especially in terms of gender gaps. To mitigate these emerging issues, digital technologies cannot be understood as autonomous, gender-neutral tools, but should be seen in a wider, socio-political context that shapes design, purpose, and use. We need to move beyond the question of access and affordability to address questions of power and inequality. That means addressing potential implications for gender-responsive digital technology of the increasing concentration of economic and political power in the tech sector and its resistance against regulation.
With the surge of the digital revolution, there has been a rise of gender inequalities for women in the educational sphere throughout the educational life-course, such as limited access in under-developed economies to educational opportunities, digital illiteracy, limited women in science, and technology, engineering, and mathematics (STEM) programs. Furthermore, ‘masculine’ cultural associations with STEM education and related gendered identities, stereotypes, and biases can complicate women’s positions.
Furthermore, there is a problem of access for women in the technology labor market, because of existing stereotypes and gendered spaces, shaped by gender power relations and associations in the educational sphere that are brought forward and are even magnified in the workplace. This in turn reduces women’s possibilities to harness the digital revolution for their empowerment, as well as poses a risk of widening the gender gap as tech giants dominate the new global economy. The structural inequality of opportunity for women in the workplace limits their participation in the design and development of new technologies, which further reproduces biases against women. Moreover, these processes are intensified by Artificial Intelligence (AI) systems automating existing job roles and the presentation of these technologies as ‘objective’ decision-making tools.
The introduction of digital technologies into the public sector, such as social protection and the welfare system has led to attempts at cutbacks and rationalization of welfare rather than the expansion of it. With women having greater domestic and care responsibilities, they are disproportionately more likely to be in need of these services. With AI being used to automate decisions in healthcare systems, legal systems, and other social policy areas, it has an immense effect on people’s lives.
Thus, it is important to explore the potential benefits of digital technology and automation for gender equality, and its possible problems and challenges to map (in)equality through social and welfare services. With deep learning systems trained on data that contain gender biases, these biases will be reproduced in the software. Yet, they are presented as ‘neutral’ decision-makers. And for now, there is a lack of transparency from the tech companies and the digital public services. Consequently, algorithms widely used in determining life-affecting circumstances can have a negative effect in that it reproduces inequalities. There is a need for genuine accountability mechanisms, external to companies and accessible to populations.