When discrimination is baked into algorithms

A recent ProPublica analysis of The Princeton Review’s prices for online SAT tutoring shows that customers in areas with a high density of Asian residents are often charged more. When presented with this finding, The Princeton Review called it an “incidental” result of its geographic pricing scheme. The case illustrates how even a seemingly neutral price model could potentially lead to inadvertent bias—bias that’s hard for consumers to detect and even harder to challenge or prove.


So how will the courts address algorithmic bias? From retail to real estate, from employment to criminal justice, the use of data mining, scoring software, and predictive analytics programs is proliferating at an exponential rate. Software that makes decisions based on data like a person’s zip code can reflect, or even amplify, the results of historical or institutional discrimination.“[A]n algorithm is only as good as the data it works with,” Solon Barocas and Andrew Selbst write in their article “Big Data’s Disparate Impact,” forthcoming in the California Law Review. “Even in situations where data miners are extremely careful, they can still affect discriminatory results with models that, quite unintentionally, pick out proxy variables for protected classes.”