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| Federal Trade Commission | |
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| diversity.¹⁷⁶ Xerox now uses an online evaluation tool developed by a data analytics firm to assess applicants, in addition to conducting interviews, to determine which applicants are most qualified for available jobs.¹⁷⁷ In developing this new assessment process, Xerox also learned that previous similar employment experience—one of the few criteria that Xerox had explicitly prioritized in the past—turns out to have no bearing on either productivity or retention.¹⁷⁸ | |
| In addition, state and local government entities are using big data to help underrepresented communities obtain better municipal services. For example, states are using big data to identify the needs of lesbian, gay, bisexual, and transgender individuals and to create more tailored approaches to reduce health disparities impacting these individuals.¹⁷⁹ And big data was used to convince a city to redraw its boundaries to extend city services to historically African-American neighborhoods.¹⁸⁰ As these examples show, organizations can use big data in ways that provide opportunity to underrepresented and underserved communities. | |
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| Summary of Research Considerations | |
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| In light of this research, companies already using or considering engaging in big data analytics should: | |
| Consider whether your data sets are missing information from particular populations and, if they are, take appropriate steps to address this problem. | |
| Review your data sets and algorithms to ensure that hidden biases are not having an unintended impact on certain populations. | |
| Remember that just because big data found a correlation, it does not necessarily mean that the correlation is meaningful. As such, you should balance the risks of using those results, especially where your policies could negatively affect certain populations. It may be worthwhile to have human oversight of data and algorithms when big data tools are used to make important decisions, such as those implicating health, credit, and employment. | |
| Consider whether fairness and ethical considerations advise against using big data in certain circumstances. Consider further whether you can use big data in ways that advance opportunities for previously underrepresented populations. | |
| 176 See, e.g., Tim Smedley, Forget the CV, Data Decide Careers, FIN. TIMES (July 9, 2014), http://www.ft.com/cms/s/2/e3561cd0-dd11-11e3-8546-00144feabdc0.html#axzz373wnekp7. | |
| 177 See, e.g., Peck, supra note 143. | |
| 178 Id. | |
| 179 See, e.g., Future of Privacy Forum Comment #00027, supra note 23, attached report entitled, BIG DATA: A TOOL FOR FIGHTING DISCRIMINATION AND EMPOWERING GROUPS, at 4; Computer & Commc’ns Indus. Assoc. Comment #00070, supra note 141, at 6–7. See also Laura Nahmias, State Agencies Launch LGBT Data-Collection Effort, POLITICO N.Y. (July 24, 2014), http://www.capitalnewyork.com/article/albany/2014/07/8549536/state-agencies-launch-lgbt-data-collection-effort. | |
| 180 See, e.g., Future of Privacy Forum Comment #00027, supra note 23, attached report entitled, BIG DATA: A TOOL FOR FIGHTING DISCRIMINATION AND EMPOWERING GROUPS, at 3. | |
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