OECD Policy Brief: Algorithm and Eve: How AI will impact women at work

6 December 2024

Female and male workers face roughly the same occupational exposure to AI overall. However, o Women are underrepresented in the occupations with the very highest exposure to AI (e.g. science and engineering professional, chief executive). Clerical occupations are not only characterised by high exposure to AI, but by an overrepresentation of women and particularly those without tertiary education.
Employment growth between 2012 and 2022 was more rapid in those occupations most exposed to AI. Women’s employment growth was even higher than men’s in occupations highly exposed to AI, reflecting women’s entry into traditionally male-dominated occupations.
Women are still underrepresented in the AI workforce (the narrow set of workers with the skills to develop and maintain AI systems), among AI users (a broader category capturing workers who say that they interact with AI at work in one way or another) and among ICT graduates. Women report less positive perceptions about AI than men.
This policy brief puts forward policy options to ensure that women and men alike can benefit from AI at work, including: applying a gender lens when monitoring AI’s impact; following an inclusive approach to upskilling and reskilling; bridging gender divides in tech; combatting AI-induced bias; and using AI to combat bias.

This policy brief puts forward the following policy options to ensure that women and men alike can benefit from AI at work:

Apply a gender lens when monitoring AI’s impact: As new AI advances emerge and as firms’ AI use broadens and matures, policymakers and researchers will want to monitor AI’s impact on employment outcomes through a gender lens. Groups facing disproportionate harm and requiring support could include non-tertiary-educated female workers in clerical occupations highly exposed to AI and non-tertiary-educated male workers in occupations at high risk of automation.

Follow an inclusive approach to upskilling and reskilling: Policymakers will want to equip workers, female and male, with the right skills so they are empowered to work with AI, adapt to changes on the job, or move from declining sectors and occupations into to new and growing ones. Reskilling and upskilling workers is crucial to seize the benefits of AI and to ensure a fair transition for workers, and this should be done in an inclusive and accessible manner.

Bridge gender divides in tech: An additional targeted effort may be needed to bridge the divides that currently hold women back from opportunities associated with AI, for instance: imbedding inclusivity into school curricula and teacher training; following a skills-first hiring approach and mandating diversity on recruitment panels; developing metrics for employers to track diversity and performance; building an inclusive work culture; providing flexible work options; supporting women-led tech businesses; and promoting role models for women in the tech sector.

Combat AI-induced bias: Policymakers must ensure that existing anti-discrimination legislation is suitable for governing AI systems and must be vigilant about the potential for gaps or loopholes to be exploited. Poorly designed or biased AI systems, trained on selective and insufficiently diverse data, can amplify labour market biases, including gender biases.

Use AI to combat bias: AI itself may offer solutions for opening up new opportunities for traditionally underrepresented groups, by identifying human bias and discrimination and offering new data-driven methods to inform decision-making. The EU AI Act calls on EU member countries to support and promote research and development of AI solutions in support of socially beneficial outcomes, such as AI-based solutions to tackle socio-economic inequalities.