A 50-page report released Jan. 6, 2016, by the Federal Trade Commission challenges business owners and consumers to evaluate the benefits and risks of data analytics. Big Data: A Tool for Inclusion or Exclusion? poses tough questions about how data is collected and analyzed and where it is stored at the end of its useful life. The report stated that big data's lifecycle typically spans four stages: collection, compilation and consolidation, analysis, and use.
"Big data's role is growing in nearly every area of business, affecting millions of consumers in concrete ways," said FTC Chairwoman Edith Ramirez. "The potential benefits to consumers are significant, but businesses must ensure that their big data use does not lead to harmful exclusion or discrimination."
The issues covered in the FTC report were initially explored in a workshop of the same title held Sept. 15, 2014, and have remained at the forefront of FTC efforts to enforce best practices in big data usage in conformance with the Fair Credit Reporting Act.
Following are topics covered in the 2014 workshop and subsequent 2016 report: How are organizations using big data to categorize consumers?
The FTC solicited opinions from the public pertaining to data analytic trends in business and private sectors, particularly in the areas of health care, education and credit scoring. The commission also cited numerous ways in which big data can help underserved communities improve access to services in health care, education, employment, and alternative forms of credit and nonbank financing.
The report also took a cautionary stance regarding how inaccurate profiles and biases used in data analytics and credit reporting can impact and marginalize individuals and groups. These outcomes can extend from basic denial of credit and privacy issues to cybercriminals targeting vulnerable consumers.
The FTC urges business owners to adhere to regulatory guidelines issued by the Fair Credit Reporting Act, the FTC Act and equal opportunity laws that govern the use of big data. The report provides guidance on how to assess levels of compliance with these laws.
The following four policy questions cited in the report are designed to help companies examine potential biases and determine their level of compliance with legal and ethical guidelines related to big data usage:
While, at first, the algorithms appeared to create accurate predictions of where the flu was more prevalent, it generated highly inaccurate estimates over time. This could be because the algorithm failed to take into account certain variables. For example, the algorithm may not have taken into account that people would be more likely to search for flu-related terms if the local news ran a story on a flu outbreak, even if the outbreak occurred halfway around the world.
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