Lenard Files Comments with FTC on Big Data and Discrimination
Contact: Amy Smorodin
July 28, 2014 – There is no evidence that big data results in discrimination against particular groups, states Technology Policy Institute President Thomas Lenard in comments filed with the Federal Trade Commission. The use of big data instead actually benefits lower-income consumers and helps companies make more accurate decisions. The comments are in response to a call for input on issues related to the upcoming workshop, “Big Data: A Tool for Inclusion or Exclusion?”
In his comments, Lenard counters two themes reflected in the Commission’s plan for the workshop. “First, that the use of big data to develop predictive models may be harmful to consumers; and, second, that the use of big data for marketing and other purposes may favor the rich over the poor or in other ways be discriminatory.”
“The claim that predictive models harm consumers, if valid, would apply to quantitative analysis used for decision-making throughout the economy,” Lenard explains, citing the use of academic test scores, credit scores, and marketing lists. He ascertains that concerns over the use of big data seem to be about the accuracy of predictive models. “Big data, however, can be expected to improve accuracy,” he explains. “The use of more data points makes it less likely that any single data point will be determinative, and more likely that a correct decision will be reached.”
There is also no evidence that the use of big data results in discrimination against lower-income consumers. “Writers who argue that data collection and analytics favor the rich over the poor rely on hypothetical rather than actual examples,” Lenard explains. Moreover, the use of big data to facilitate price discrimination is more likely to favor lower-income individuals. “Since price discrimination involves charging different prices to different consumers for the same product based on their willingness to pay,” he states, “and since willingness to pay is generally positively related to ability to pay, price discrimination will, other things equal, result in lower prices to lower-income consumers.”
Big data are being used to develop products that specifically benefit lower-income consumers. For example, some companies are using models containing more variables than traditional credit scoring to help lenders determine whether or not to loan to people who are otherwise poor credit risks. Other companies help consumers compare retailers to find the lowest price on a product.
“The Commission asks whether big data is a tool for inclusion or exclusion and, of course, it is both,” Lenard concludes. “Any tool that categorizes individuals will necessarily include some and exclude others. There is, however, no evidence that big data unfairly favors or disfavors particular groups.”
The comments and related TPI paper “Big Data, Privacy and the Familiar Solutions” submitted with the comments are available on the TPI website.
The Technology Policy Institute
The Technology Policy Institute is a non-profit research and educational organization that focuses on the economics of innovation, technological change, and related regulation in the United States and around the world. More information is available at https://techpolicyinstitute.org/.