AI is already transforming the labor market and its influence will only increase.  The structural gender asymmetry of certain fields and roles means that it will proportionally impact men and women differently. Generative AI puts at risk any roles which primarily focus on routine language processing like administrative, clerical, customer service, and data-entry roles. These support roles exploded with the increase of paperwork, filing, and bookkeeping as corporations grew during the Industrial Revolution. The work was facilitated by commercial typewriters adopted in the 1870s and these positions were primarily held by women who were trained in typing skills.  These roles initially offered women their entry into the corporate workforce and the gender based configuration of the labor market today still echoes this configuration. This lingering historic disproportionality is the underpinning of concerning statistics like the ILO’s finding that women’s jobs are twice as likely to be exposed to AI automation risk as men’s.

Women’s systemic professional vulnerability to AI is compounded by the early statistics on the gender imbalance of AI adoption.  Due to cited factors of increased fears of negative perception, ethical concerns and reliability of output, a Harvard Business School study shows that women are integrating AI into their workflows at a lower rate than men. As the workforce continues to adapt to AI, this gap will amplify existing gender pay inequality, as AI skills not only create greater job security but also command a wage premium. A few options exist to fight back against the disproportionate impact that workforce displacement from AI will have on women. Companies can offer AI training sessions, paid learning hours, dedicated digital testing environments and clear managerial endorsements for workers looking to expand their skills.  Policy makers can implement tax incentives or public subsidies to support companies that commit to upskilling or reskilling displaced operational workers instead of conducting layoffs. And on a structural level, STEM pipeline programs can continue to expand support for women entering or transitioning into AI in order to reduce internal demographic bias in the field and the tools it produces.

Text: Emily Genatowski