Popular Culture Review Volume 30, Number 1, Winter 2019 | Page 181

The Perils of Algorithmic Hiring and Title VII
potentially employee . Employers browsing candidate ’ s social media has been an accepted reality , and now the further rise of nontraditional employee data is raising concerns . Nontraditional employment data are a collection of information “ maintained by the employer , public records , social media activity logs , sensors , geographic systems , internet browsing history , consumer data-tracking systems , mobile devices , and communications metadata systems .” 21 Other sources of data , such as combinations of words on resumes , personality test results , facial recognition software , and individual performance ratings on tests can also be considered . 22 Companies may also choose to include “ internal company information such as frequency of meetings , locations of meetings , recipients and content of employee emails , and records of employee participation in wellness programs .” 23
This list of data sources continues to expand with information such as a person ’ s face and voice being reduced to a string of code . 24 All of this information is gathered , quantified , and combined with other sources for use by employers . Potential employers may gather the data themselves or purchase it from information brokers . From there , the data can be used to uncover underlying patterns for use in predicting outcomes for similarly profiled groups of employees or applicants . 25
Facial recognition and voice analysis data are likely to prove especially problematic because it may allow companies to potentially sidestep Title VII racial protections by favoring datasets that match certain ethnicities and exclude others . Distilling down a person ’ s facial features into data points would allow for potential comparison to an “ ideal .” A potential employer could ask an algorithm to produce candidates with facial features indicating only those of Asian-American descent , with a voice or speech pattern . These data points
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