According to the Federal Trade Commission (“FTC”), companies using big data analytics face questions about both legal compliance and broader big data policy issues. In a report, Big Data: A Tool for Inclusion or Exclusion? , released on Wednesday, January 6, the FTC synthesized lessons from a fall workshop, sixty-five public comments, and an earlier FTC seminar on alternative credit scoring products in an effort to educate business on some of the risks and opportunities around big data.

Before addressing the FTC’s specific legal and policy considerations, much of the report discusses the “life cycle” of big data and a number of potential uses, positive and negative, for big data. Some of the specific privacy risks worth highlighting include the FTC’s concern that analytics products could be used to expose sensitive information by predicting sexual orientation or religious affiliation. Following up on the White House’s report on big data and differential pricing, the report notes that big data could be used to implement new forms of price discrimination on low-income consumers. Big data could also inadvertently weaken the effectiveness of consumer choice by allowing companies to draw inferences about consumers in spite of their efforts to opt-out of data collection.

In response to these concerns, the FTC advises companies to be aware of how big data practices could be covered by existing consumer protection laws like the Fair Credit Reporting Act (“FCRA”), an array of equal opportunity laws, and the FTC Act. It stresses that any regulatory inquiry under these laws remains highly fact-specific.

• Fair Credit Reporting Act: The FCRA applies to companies that compile, sell, or use consumer reports to make eligibility determinations for certain benefits or transactions, and the report notes that companies are increasingly relying upon predictive big data analytics products for eligibility determinations. The FTC highlights several examples where the involvement of third-party analytics services or the use of non-traditional information such as social media data implicate the FCRA. The report also disputes an earlier position by the FTC that information that does not identify a specific consumer may not be a consumer report where that information is used in any part to analyze eligibility.
• Equal Opportunity Laws: Considerable space is devoted to discussing the applicability of different equal opportunity laws to big data analytics, and that disparate impact analysis under these laws may be especially important within the context of big data. (Disparate impact occurs when companies employ a facially neutral practice that has a disproportionate effect on a protected class, absent a legitimate business need that cannot be achieved by less disparate means.) The FTC also cautions that advertising and marketing practices can implicate equal opportunity laws for creditors, and marketing that impacts subsequent lending patterns or the terms and conditions of a credit offer can be cited as evidence of discrimination.
• Section 5 of the FTC Act: Finally, the FTC reiterates its position that companies must ensure their use of big data analytics is not unfair or deceptive to consumers. The report stresses the need for reasonable security measures and that companies must be on guard against selling or sharing analytics products or services if they know (or have reason to know) their customers could be using them for discriminatory or fraudulent purposes.

Beyond legal compliance, however, the report also discusses a set of special policy considerations raised by big data. Worried that errors and biases can emerge and expand throughout the entire big data life cycle, the FTC encourages businesses to think about the following four questions:

1. How representative is your data set? Citing the Boston Street Bump application that was designed to detect potholes via a smartphone app, the FTC explains that once the app team realized that because lower income individuals were less likely to carry smartphones, they also realized their application data was not representative of road conditions across Boston. According to the FTC, companies should be aware of how “digital divides” and “data deserts” could produce skewed and unfair results.
2. Does your data model account for biases? Even prior to the widespread use of big data, computer models could reproduce existing biases in employment determinations simply by incorporating pre-existing discriminatory actions into new decision-making. The FTC recommends companies think carefully about how both data sets and algorithms are being generated.
3. How accurate are your predictions based on big data? The FTC cautions that while big data has improved the ability to detect correlations among data points, it cannot always explain which correlations are meaningful. The report highlights efforts by lenders to improve access to credit by using non-traditional indicators such as rental or utility bill payment history, but notes that there could be legitimate reasons for consumers to withhold paying or otherwise dispute a bill, which could throw off these innovative credit models.
4. Does your reliance on big data raise ethical or fairness concerns? The notion that big data analytics raises larger ethical issues emerged in the White House’s 2014 Big Data Report and continues to be of concern to the FTC. The report suggests that companies consider assessing what factors go into an analytics model and balance any predictive value against fairness considerations. It also asks companies to consider how they might deploy big data in ways to advance opportunity.

It is worth noting that Commissioner Ohlhausen issued a separate concurring statement, acknowledging concerns about big data analytics but encouraging policymakers to “test hypothetical harms with economic reasoning and empirical evidence.” She argues that big data analytics may actually combine with competitive markets to resolve rather than exacerbate industry’s misunderstandings of low-income populations.

In any event, this latest report continues the FTC’s pattern of interest in exploring how big data practices could have detrimental effects on low-income and underserved populations, and promises further enforcement under the legal regimes cited above.