For the future of work to be inclusive, efforts are required to promote diversity and address the low level of participation of women at all stages of the development of Fourth Industrial Revolution technologies such as artificial intelligence and automation. Brigid Trenerry, Gayathri Haridas and Sun Sun Lim of the Singapore University of Technology and Design stress the importance of preventing biases and existing prejudices from being built into technological applications.
Every time a person instructs Siri or Alexa to do something, that user might unwittingly be compounding gender inequality. A United Nations report suggests that when digital home assistants come with a default female voice, they perpetuate the stereotype that women must be helpful, accommodating and downright subservient. The encoding of such biases into everyday technologies can only exacerbate gender disparities and perceptions as we enter an era of intensifying digitalization.
With the growing turn toward artificial intelligence (AI) and automation, the future of work and its impact on different groups across social and economic divides is riven with uncertainty. Economists predict that the current wave of disruption – dubbed the Fourth Industrial Revolution – will be markedly greater than previous swells of technological change. While the scale and pace of disruption are still hotly debated, there is increasing understanding of which jobs are more prone to automation and what kinds of skills are needed in the digital economy.
But what needs to be done to ensure that the future of work is inclusive?
In Asia, the uptake of digital technologies has grown significantly in the past two decades, with countries such as Japan, Singapore, China and South Korea leading in AI research and innovation. Middle and upper-middle income countries such as Indonesia, Malaysia and India are also investing heavily in new technologies and will benefit from their large populations of younger workers, provided that education and skills development are effectively harnessed. Meanwhile, a number of Asian countries face challenges of an ageing population, where new programs that equip older adults with digital skills, alongside programs to address barriers to workforce participation, such as ageism, are required.
In this rapidly shifting and disrupted demographic and technological landscape, fresh commitments to diversity and inclusion are necessary to ensure that no-one is left behind in the digitalized workplaces and markets.
But championing inclusion is by no means a mere exercise in honoring ideals. There is a clear business case for diversity. A recent report found that increased commitment to gender equality in Asia-Pacific could help to grow gross domestic product in the region by 12 percent, or US$4.5 trillion, by 2025. Company-level studies have shown that increased gender and racial diversity lead to increased company profits and innovation, with heterogeneous teams outperforming homogeneous ones and enhancing creativity and innovative thinking. Increased diversity within science and research can also drive scientific discovery and lead to new solutions in dealing with complex global challenges, such as climate change. It is clear that diversity is no longer just an add-on but should be viewed as an essential driver of productivity and growth in increasingly diversified and globalized markets.
Despite these benefits, barriers to the full participation and inclusion of women and other under-represented groups remain. For women, a recent global report found that while some gains had been made, it will take more than 200 years to close the pay gap between men and women, due to the slow pace of change and the enormity of the gap in some occupations and industries. Gaps in pay and workforce participation can be attributed to a combination of factors, from the disproportionate share of unpaid care and domestic labor borne by women to occupational segregation and workplace bias and discrimination. More studies, including experimental designs that gauge employer hiring decisions, are needed to disentangle different factors contributing to gender inequality in Asia.
New technologies can empower traditionally under-represented groups such as women, both in the labor market and in society, by creating new opportunities for access and participation. Automation, however, might also work to widen existing gaps in wages and workforce participation, depending on the extent to which women can transition into high-skilled occupations in technical fields and in enterprises. Navigating the road ahead involves looking at existing patterns of occupational segregation, including the extent to which men and women are clustered in occupations and industries more prone to automation.
Gender-based skills gaps are most keenly felt in the artificial intelligence sector, where only 22 percent of professionals globally are women.
The effects of automation will vary between country and regional contexts. A recent study of G20 countries found that the risks of automation are not evenly distributed between men and women. For low-skilled workers, the authors found that new technologies will replace women’s jobs to a lesser extent than men’s jobs because jobs currently held by low-skilled women, such as in healthcare and domestic work, are less prone to automation than those held by low-skilled men, such as machine operators or assembly-line workers.
Among middle and higher-skilled workers, women are largely under-represented in areas that are set to grow in the digital economy, such as high-demand technical and entrepreneurial roles. Another multi-country study estimated that female workers face a higher risk of automation compared to male workers overall. The danger level differs across sectors and countries, with less educated and older female workers, as well as those in low-skilled clerical, service and sales roles in greater jeopardy of losing their jobs due to automation.
Gender-based skills gaps are most keenly felt in the AI sector, where only 22 per cent of professionals globally are women. Some countries fare better, including Singapore, where 28% of the AI talent pool is female, slightly above the global average. Much more needs to be done to attract and retain women in AI, as well as in other science, technology, engineering and mathematics (STEM) occupations.
It is vital that women and other under-represented groups participate in both the creation and application of AI technology, given the increasing recognition of the technology’s biases. Notably, researchers have found gender and racial bias in everything from facial recognition software to recruitment tools and mortgage applications. Also known as algorithmic bias, such preferences can be built in or can develop at various stages of the deep-learning process, including when AI professionals frame problems and solutions or when data being used to train AI somehow reflect predispositions, prejudices and discrimination.
Standard computer science practices are not designed to monitor and detect such biases. As AI algorithms are given more and more power to guide decision-making in people’s lives, it is crucial to ensure that the benefits of new technologies outweigh the harms. Ensuring a balance of diversity in the development of AI will mean that previously overlooked problems of bias will be unearthed, making new technologies more responsive to diverse clients and stakeholders. While much of the discussion is centered on the future of work, it is vital to focus on how automation will affect the larger social and economic divides already prevalent in society.
Only by closing the gender divide in technological development would it be possible to establish firmly and rightfully a place for women at the drawing board so that people issue commands not just to an Alexa but to an Adam too.
This article is based on a presentation on the gender implications for the future of work, which the authors delivered at the 16th Gender Summit, held in Singapore on August 28-29, 2019. This research was supported by a grant from the Social Science Research Council. Any findings, conclusions, recommendations, or opinions expressed are those of the authors only.
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