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Federal Trade Commission

Federal Trade Commission, Big Data:  A Tool for Inclusion or Exclusion?


In January 2016 the Federal Trade Commission released a report on “big data,” which has implications for employers. “The term ‘big data’ refers to the nearly ubiquitous collection of consumer data from a variety of sources . . . to draw connections and make inferences and predictions.” Basically, companies track the digital activities of consumers to analyze trends and tastes.

When analyzed, big data can provide surprising insights. For example, one study showed that “people who fill out online job applications using browsers that . . . had to be deliberately installed (like Firefox or Google’s Chrome) perform better and change jobs less often.” Most employers would find this information useful, but some may not realize the legal implications of making hiring decisions based on this information. “If an employer were to use this correlation to refrain from hiring people who used a particular browser, they could be excluding qualified applicants for reasons unrelated to the job at issue.”

In other words, employers could be actively discriminating against applicants based on their age, race, gender, disability, and even credit history.

Here is why: one way to prove discrimination is to show that an employer treated an applicant differently based on protected characteristics (disparate treatment). While an employer may be tempted to act on illuminating data, it must be careful to “not disfavor a particular protected group because big data analytics show that members of this protected group are more likely to quit their jobs within a five-year period.”

Even facially neutral decisions can be discriminatory toward a protected group (disparate impact). Big data analytics “can reproduce existing patterns of discrimination, inherit the prejudice of prior decision-makers, or simply reflect the widespread biases that persist in society.” One academic found that Google searches of names “associated with African-Americans” were more likely to generate “advertisements for arrest records” compared to “names associated with white Americans.”

It is exciting to live in a dynamic and digital world. Employers should recognize that discrimination laws can be just as dynamic. “Companies should therefore think carefully about how the data sets and the algorithms they use have been generated. Indeed, if they identify potential biases in the creation of these data sets or the algorithms, companies should develop strategies to overcome them.”[Federal Trade Commission, Big Data: A Tool for Inclusion of Exclusion? January 2016].