New research shows that Facebook’s ad-distribution software is disturbingly biased
ON MARCH 28th the American government sued Facebook for allowing advertisers to exclude whole categories of people from seeing ads for housing—couples with children, non-Americans, non-Christians, disabled people, Hispanics, and so on. The Department of Housing and Urban Development said this violated the Fair Housing Act, which bans discrimination against certain “protected” groups.
The researchers also found a disparity based on gender: jobs for supermarket attendants and janitors tended to be delivered to women, whereas ads for lumberjacks were more likely to be delivered to men. Advertising relies to a large extent on trying to reach particular groups of people. Sellers of luxury watches want to sell to rich people, for instance, who are more likely to be white than black. But the ability of algorithms to reach the intended audience by sifting vast amounts of personal data is causing growing dismay. This is especially true of Facebook because of its scale relative to traditional media. Moreover, its advertising systems are too complex to be understood at a glance.
The researchers take pains to point out that they are not making sweeping claims about Facebook’s entire ad-delivery system, given that they monitored its behaviour in only a few situations. There is no suggestion that Facebook designed its systems to discriminate intentionally. But its machine-learning software, in the process of training itself on the data of Facebook’s users in order to tailor ads to their interests, appears to have absorbed some of their prejudices.
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