From the course: Ethics and Law in Data Analytics

Consumers and policy

From the course: Ethics and Law in Data Analytics

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Consumers and policy

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- According to the Consumer Financial Protection Bureau, as many as 11% of U.S. consumers are "credit invisible," meaning they do not have enough up to date repayment history for an algorithm to produce a credit score so that they can access credit. Data analytics can actually alleviate this problem by using new data inputs such as other bill payments like phone bills, public records, education, social media, et cetera, and can improve chances for otherwise ineligible consumers to receive credit and loans. This could have a positive impact on African-Americans and Latinos who are more likely to be credit invisible than whites, but if not used with due care these new scoring mechanisms could actually make the problem worse and perpetuate or mask discrimination. Remember, the challenges here are data inputs and accuracy and lack of transparency with respect to the design and workings of the system. As algorithms develop to measure credit worthiness in new ways it will be critical to design and test them to guard against carelessly using information that's a proxy for race, gender, and other protected characteristics. There may be legal protection here for consumers in negligence law, and in federal statutory law, such as the Fair Credit Reporting Act and the Equal Credit Opportunity Act, but it is really challenging for consumers, particularly those with less experience dealing with large institutions or complex data products to even identify when and if a violation has occurred.

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