Artificial intelligence (AI) will transform the workplace; the question is how. Until now, estimates of AI’s effects have been commingled with analyses of automation and software impacts. Stanford researcher Michael Webb has found a way to coax out granular answers regarding the potential effects of AI on particular occupations. The method yields scores for exposure level to impact from AI on specific jobs, roles, demographic groups and communities. Senior fellow Mark Muro, research analyst Jacob Whiton and research associate Robert Maxim of the Metropolitan Policy Program at the Brookings Institution report on the Stanford findings in their fascinating white paper.
A researcher has used a new approach to predict the effects of AI on the labor market.
Michael Webb of Stanford University has devised a novel statistical method of assigning exposure scores to occupations. The data, when combined with other workforce information, make possible granular predictions of AI’s future impact on labor markets. Webb used AI to compare the tasks mentioned in AI patents to the US Department of Labor’s Occupational Information Network (O*NET) database occupational descriptions. Where these overlap, Webb predicts that AI has potential to affect the occupation. A high exposure score indicates a likelihood of AI exerting either positive or negative impact. The method can’t predict whether, or in what way, that potential will play out. For example, AI could benefit an occupational group by automating certain tasks, thereby giving workers the possibility of taking on richer roles. And AI could generate new kinds of jobs, just as the invention of the automobile did.
AI will affect the workforce in different ways than it affects robotics and software.
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About the Author
Mark Muro is a senior fellow, Jacob Whiton is a research analyst and Robert Maxim is a research associate in the Metropolitan Policy Program at the Brookings Institution.