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46 Fla. St. U. L. Rev. 457 (2018-2019)
Fair Housing Act at 50: Challenging the Disparate Impact of Predictive Analytics

handle is hein.journals/flsulr46 and id is 477 raw text is: FAIR HOUSING ACT AT 50: CHALLENGING THE DISPARATE IMPACT OF PREDICTIVE ANALYTICS KODY GLAZER ABSTRACT The year 2018 marked the 50th Anniversary of the enactment of the Fair Housing Act. Alt- hough there have been mixed reviews on the success of the Act in reaching its goals of eradicating discrimination from the housing market and of affirmatively furthering fair housing, one thing remains clear-the Act must evolve and react to changing technologies to reach its full potential in the twenty-first century. Gone are the days where housing providers violate the Act by advertis- ing homes to whites only or with statements that say, Irish need not apply. Today, housing providers can discriminate by relying on predictive algorithms that feed off of the massive amounts of data-gathering techniques that exist in the digital world. In- stead of refusing to advertise to Latino or African-American families outright, housing pro- viders can devise algorithms that exclude Latinos or African-Americans based on stand-in data or proxies. Given the secretive nature of these algorithms, it can be nearly impossible to prove an intent to discriminate based on a protected class. This Note explores how the disparate-impact theory of liability under the Fair Housing Act can be used to challenge discriminatory algorithms and the data that forms them, spe- cifically in regard to advertising. This Note explores how some data points within algo- rithms exist only to create disparate impacts on protected classes without having any ties to a legitimate housing purpose. Part I will introduce the scope of the issue and the detrimental effects that housing seg- regation can have on our communities. Part II will provide an analysis of the Fair Housing Act and how it applies to discriminatory advertising. Part III will describe big data, predic- tive analytics, and how housing providers have the potential to gather large amounts of information on individuals in the housing market. Finally, Part IV will apply the Fair Housing Act's disparate-impact theory of liability to the algorithms that shape decisions about how to advertise housing and discuss the challenges within. Addressing the disparate impact of predictive analytics will be difficult in practice. Yet, in the era of Big Data, it is essential to be at the forefront of changing technologies to protect those who are most vulnerable in our society. I. INTRODUCTION.............................. .............................. 458 II. THE FAIR HOUSING ACT .................................................... 460 A. The FHA's Prohibition Against Unlawful Advertising Practices............... 460 B. Section 804(c) Should Cover Advertising Decisions Made by Algorithms ............... ......................... .......... 461 C. Disparate-Impact Theory of Liability Under the FHA ..... ............. 462 D. Standing Under the Fair Housing Act ....................... ....... 464 E. Potential Communications Decency Act Concerns...................... 465 III. PREDICTIVE ANALYTICS-BIG DATA AT WORK .............. ................. 466 A. What Information is Collected and How is it Collected? The Basics ......... 466 B. How Data is Used to Create Individual Profiles.... ................... 469 C. Downsides and Potential Liability in Big Data Use........... ......... 470 IV. APPLYING A DISPARATE-IMPACT THEORY OF LIABILITY TO CHALLENGE AUTOMATED DECISION MAKING UNDER THE FAIR HOUSING ACT...................... 477 A. The Black Box of Automated Decision Making ...... .................. 477 B. Establishing Prima Facie Case of Disparate Impact-An Algorithm as the Challenged Policy ............................................ 479 C. Business Justification- Weeding Out Irrelevant Data Points ................... 480 D. Identifying Less Discriminatory Alternatives-Cleaning Up the Algorithm ......................................... ............ 482 V. CONCLUSION .............................................................. 482

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