Artificial intelligence and gender: further widening the divide?

The development of artificial intelligence poses significant risks to the labor market, especially for marginalized communities. While AI provides huge gains in efficiency and productivity, not everyone in the workforce experiences this benefit equally. With 75% of knowledge workers now using AI, there is a constant threat of workforce displacement, wage inequality and changing demand for skills. Artificial intelligence is changing the current labor market landscape and not all employees are well prepared to adapt. Occupational segregation, pay gaps and access to opportunities are now shaping the impact of the AI ​​revolution on men and women.

The impact of AI is expected to vary by industry, with some sectors more susceptible to automation than others. These include low-skilled everyday jobs such as retail, data entry and healthcare. As many such enterprises are dominated by women, the World Economic Forum highlights that these sectors are most vulnerable to AI automation. Women fear that the risk of losing their job due to automation is 11% compared to 9% of men. It is estimated that, according to the study “On the Margins – Women Workers and the Future of Work, Narratives in Pakistan” by Friedrich-Ebert-Stiftung, a foundation of a German political party.

Artificial intelligence poses a threat to the existing gender pay gap. Many high-paying STEM fields are male-dominated and are likely to see increased demand due to the rise of artificial intelligence, such as machine learning, artificial intelligence and engineering roles. The 2018 OECD report “Bridging the Digital Gender Divide” found that in most OECD countries, women are less likely to demonstrate high levels of “digital literacy” than men, and this gap widens with age as how skills development becomes much more challenging with simultaneous employment and home care responsibilities.

According to the ILO, artificial intelligence tends to increase demand for high-skilled workers, while reducing demand for middle-skilled workers and completely automating low-skilled jobs. This is a concern in developing economies where women mainly hold low- and middle-level positions.

The use of artificial intelligence in recruitment and performance evaluation has raised concerns about the potential for algorithmic bias to widen gender disparities in the workplace. Artificial intelligence systems trained on biased data can replicate and even exacerbate existing gender biases. For example, Amazon discontinued its AI recruiting tool after discovering that the algorithm favored male candidates, consistently undervaluing resumes that included the term “women.”

Moreover, AI-based monitoring systems used for performance management may disproportionately harm women who are more likely to take on caring responsibilities and work variable hours. If AI systems do not take these dynamics into account, they may unfairly penalize women for non-traditional work habits, widening the gender gap in career development.

Another critical impact of AI is the lack of resources and skills for women to learn and adapt to AI. Women make up only 34% of the STEM workforce, making it difficult for women in non-STEM fields to learn integrated AI technologies. This hinders the transition to AI and potentially hinders women’s learning.

Addressing the gender implications of AI requires targeted policy initiatives and a proactive approach. First, increasing women’s involvement in STEM education and AI-related sectors is crucial to reducing the gender gap in the labor market. Gender-responsive mentoring programs, scholarships and AI skills training can help women enter growing technology roles.

Governments and businesses must also emphasize accountability for AI, ensuring that AI systems are built and deployed in a way that reduces bias. The EU’s planned AI rules, which include rigorous scrutiny of high-risk AI applications, provide a promising basis for combating algorithmic bias. Additionally, labor regulations need to be reviewed to encourage reskilling and upskilling programs that educate workers, especially women, for the AI ​​economy.