This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Jennie Brand, a UCLA professor of sociology with an appointment in statistics and data science, is an expert on the socioeconomic effects of job loss, including the long-term economic, psychological, physiological and intergenerational effects. As the current job landscape is being transformed by artificial intelligence, Brand and her research partners at the Occupation Lab have been examining how occupational change, including AI exposure, is affecting workers' job mobility.
An AI-exposed job is one in which tasks could potentially be performed more efficiently with AI. In their recent paper, "Trapped in declining occupations: Barriers to worker mobility in a changing economy," published in Science Advances, Brand and her research team found that workers employed in growing occupations have better prospects for upward mobility than those employed in declining occupations—creating a "mobility trap" for the latter group. Findings specific to AI-exposed jobs were also published in the Papers and Proceedings of the American Economic Association.
Brand, who is also co-director of the UCLA Center for Social Statistics, and Xi Song, who received her Ph.D. from UCLA and is now a professor at Columbia University, answered questions related to their research. An AI-exposed job is one whose tasks could potentially be performed more efficiently with AI. In our research, we consider a job AI-exposed if large language models and related software could reduce the time needed for particular tasks by at least half while maintaining quality.
The number of people in an occupation highly exposed to AI might decline, grow or change substantially as workers use AI. We also distinguish AI exposure from AI skills: Having a job that AI can affect does not necessarily mean having the skills or support to work effectively with it. We highlight a particularly concerning situation: workers in occupations that are both highly exposed to AI and projected to decline in terms of the number of people in those occupations.
These workers face pressure on their existing jobs and may face limited mobility paths into expanding fields. Our work finds that changing occupations often results in moving into another declining occupation without improving their economic position, a "mobility trap." The central concern is therefore the combination of AI exposure, declining demand and limited access to better alternatives. Exposure alone is insufficient to identify who is most vulnerable.
Our preliminary research on AI skills identifies high AI exposure in occupations such as payroll clerks, insurance claims and policy-processing clerks, bookkeeping clerks, medical transcriptionists, data-entry workers, and interpreters and translators. These occupations involve information processing, documentation or language tasks. They occur across insurance, health care, accounting, administrative services and other industries.
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