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

Ian Burn, Daniel Firoozi, Daniel Ladd and David Neumark

No 28328, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: J14 J71 K31 (search for similar items in EconPapers)
Date: 2021-01
New Economics Papers: this item is included in nep-age, nep-big, nep-cmp, nep-exp, nep-law and nep-lma
Note: AG LS
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