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Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment

Author

Listed:
  • Ian Burn
  • Daniel Firoozi
  • Daniel Ladd
  • David Neumark

Abstract

We explore whether ageist stereotypes in job ads are detectable using machine learning methods measuring the linguistic similarity of job-ad language to ageist stereotypes identified by industrial psychologists. We then conduct an experiment to evaluate whether this language is perceived as biased against older workers. We find that language classified by the machine learning algorithm as closely related to ageist stereotypes is perceived as ageist by experimental subjects. The scores assigned to the language related to ageist stereotypes are larger when responses are incentivized by rewarding participants for guessing how other respondents rated the language. These methods could potentially help enforce anti-discrimination laws by using job ads to predict or identify employers more likely to be engaging in age discrimination.

Suggested Citation

  • Ian Burn & Daniel Firoozi & Daniel Ladd & David Neumark, 2021. "Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment," NBER Working Papers 28328, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:28328
    Note: AG LS
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    More about this item

    JEL classification:

    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • K31 - Law and Economics - - Other Substantive Areas of Law - - - Labor Law

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