Machine Learning and Perceived Age Stereotypes in Job Ads: Evidence from an Experiment / Ian Burn, Daniel Firoozi, Daniel Ladd, David Neumark.

Format:

Book

Author/Creator:

Burn, Ian.

Contributor:

National Bureau of Economic Research.

Firoozi, Daniel.

Ladd, Daniel.

Neumark, David.

Series:

Working Paper Series (National Bureau of Economic Research) no. w28328.

NBER working paper series no. w28328

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2021.

Summary:

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.

Notes:

Print version record

January 2021.