Bipartisan Senators Demand Federal Data on AI's Labor Market Impact

A bipartisan coalition of U.S. senators, spearheaded by Mark Warner (D-Va.) and Josh Hawley (R-Mo.), has formally requested that Department of Labor (DOL) agencies enhance their data collection efforts regarding the impact of artificial intelligence on the labor market. This initiative underscores a growing recognition within Congress of the urgent need for empirical data to inform policy in an era of rapid AI integration.

In a letter addressed to key DOL officials, including Labor Secretary Lori Chavez-DeRemer and acting Bureau of Labor Statistics (BLS) Commissioner William J. Wiatrowski, the senators specifically called for modifications to three existing federal surveys: the Current Population Survey (CPS), the Job Openings and Labor Turnover Survey (JOLTS), and the National Longitudinal Survey. The goal is to incorporate AI-related questions that can capture trends in job loss, creation, and displacement.

The impetus for this request is the acknowledged insufficiency of current federal data to accurately gauge AI's influence on the workforce, despite the technology's widespread adoption. The senators highlighted a provision in the fiscal 2026 spending package that already encourages the BLS to assess AI's economic impact, urging the agencies to build upon these directives swiftly and substantially.

This latest push follows previous legislative efforts by Warner and Hawley, including the AI-Related Job Impacts Clarity Act (S.3108), introduced in November. This bill proposes mandatory quarterly reports from federal agencies and private companies to the Labor Department, detailing AI's effects on their workforces. The bill is currently under review by the Senate Health, Education, Labor, and Pensions Committee, indicating sustained congressional interest in this issue.

The need for better data is further supported by external research, such as a Yale Budget Lab study that noted the limitations of self-reported data from AI companies. This study, while not yet showing clear impacts on employment status, emphasized the necessity of more robust information. In a related development, Anthropic, a prominent AI developer, recently introduced a new metric called "observed exposure" to measure AI displacement. Their preliminary findings suggest that workers in positions most exposed to AI automation tend to be older, female, more educated, and higher-paid. This private sector initiative highlights the potential for AI to disproportionately affect certain demographic groups, reinforcing the urgency for comprehensive public data.

The bipartisan nature of this call for data signifies a broad consensus on the strategic importance of understanding AI's labor market implications. Accurate data is critical for developing effective policies that can mitigate negative consequences, such as job displacement, and harness AI's potential to create new economic opportunities and enhance productivity, ensuring a just transition for the American workforce.

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