13 Matching Annotations
  1. May 2019
    1. “It sure smells like the prescreening provisions of the FCRA,” Reidenberg told The Intercept. “From a functional point of view, what they’re doing is filtering Facebook users on creditworthiness criteria and potentially escaping the application of the FCRA.”
    2. In an initial conversation with a Facebook spokesperson, they stated that the company does “not provide creditworthiness services, nor is that a feature of Actionable Insights.” When asked if Actionable Insights facilitates the targeting of ads on the basis of creditworthiness, the spokesperson replied, “No, there isn’t an instance where this is used.” It’s difficult to reconcile this claim with the fact that Facebook’s own promotional materials tout how Actionable Insights can enable a company to do exactly this. Asked about this apparent inconsistency between what Facebook tells advertising partners and what it told The Intercept, the company declined to discuss the matter on the record,
    3. How consumers would be expected to navigate this invisible, unofficial credit-scoring process, given that they’re never informed of its existence, remains an open question.
  2. Aug 2017
    1. To conclude, the possibility of receiving a loan increases based on our experience. In some cases, we have even seen a drop in the interest rate. This results in more people receiving access to credit with a better interest rate thanks to increase of scoring model accuracy. We believe that designing systems from the start in discrimination-conscious way will reduce the risk of machine-learning algorithms introducing unintentional bias much like humans do. This should avoid the moral problem of discrimination. In addition, requiring drivers to pass an eye test discriminates against the blind, but eyesight is quite essential to safely drive a car. As the last exclusion is justified then loaning to people who are not creditworthy should be an acceptable exclusion as well.
    2. big data scoring credit score financial inclusion