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One credit reporter from Buffalo, New York noted that “prudence in large transactions with all Jews should be used,” while a reporter in Georgia described a liquor store he was profiling as “a low Negro shop.” Similarly, the Retailer Credit Company, founded in 1899 (now Equifax) made use of information gathered by Welcome Wagon representatives to collate files on millions of Americans for the next 60 years. A map from that year of Greater Atlanta comes color-coded in shades of blue (desirable), yellow (definitely declining) and red (hazardous). The legend recalls a time when an individual’s chances of receiving a mortgage were shaped by their geographic status. The neighborhoods that received a hazardous rating were frequently poor or dominated by racial and ethnic minorities. By 1935, whole neighborhoods in the US were classified according to their credit characteristics. The scoring practice, known today as redlining, acted as a device to reduce mobility and to keep African American families from moving into neighborhoods dominated by whites. The Fair Credit Reporting Act in 1970 and the 1974 Equal Credit Opportunity Act were attempts to rectify these discriminatory practices. Today, or so the fintech narrative goes, we have detailed and unbiased scoring algorithms that are perceptually blind to gender, class, and ethnicity in their search for a creditworthy individual. And yet, burgeoning studies of how algorithms classify and make decisions mirror these historic geographies of exclusion, leading academics such as Cathy O’Neill and Frank Pasquale, who study the social, economic, and political effects of algorithmic decision making, to point to emergent practices of “weblining,” where algorithmic scores reproduce the same old credit castes and inequalities. Because these systems learn from existing data sets, it often follows that existing bias shapes what the machine decides is good, bad, normal or creditworthy. The Chinese government is now close to launching its own algorithmic “Social Credit System” for its 1.4 billion citizens, a metric that uses online data to rate trustworthiness. These systems are fast becoming the norm. This is to say nothing of the legitimacy of using such algorithmic proxies in the first place. As these systems become pervasive, and scores come to stand for individual worth, determining access to finance, services, and basic freedoms, the stakes of one bad decision are that much higher. While it might seem obvious to call for greater transparency in these systems, with machine learning and massive datasets it’s extremely difficult to locate bias. More important than understanding how these scores get calculated is giving users meaningful opportunities to dispute and contest adverse decisions that are made about them by the algorithm. Maybe then we can really see if these systems are giving credit where credit is due. Even if we could peer inside the black box, we probably wouldn’t find a clause in the code instructing the system to discriminate against the poor, or people of color, or even people who play too many video games. This article was originally published on Undark. Read the original article.
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