UNITED STATES OF AMERICA

Federal Trade Commission

WASHINGTON, D.C. 20580

Office of Commissioner

Rohit Chopra

October 16, 2019 

The Honorable Ben Carson 

Secretary

U.S. Department of Housing and Urban Development 

451 7th Street SW 

Washington, DC 20410-0001 

RE: Proposed Rule to Amend HUD’s Interpretation of the Fair Housing Act’s Discriminatory 

Effects Standard 

Dear Secretary Carson: 

I write to share a comment I submitted today that outlines concerns with HUD’s proposed rule amending 

the Fair Housing Act’s discriminatory effects standard. This proposal appears to fundamentally 

misunderstand how algorithms, big data, and machine learning work in practice. It would provide safe 

harbors to the same technologies at issue in HUD’s own action against Facebook, a complaint which 

details the many ways that platforms can discriminate by design. 

My comment outlines three arguments against HUD’s proposed changes. First, algorithms are not neutral, 

and even valid inputs can produce discriminatory results. Second, it is inappropriate to create safe harbors 

around technologies that are proprietary, opaque, and rapidly evolving. Finally, outsourcing liability for 

algorithmic discrimination to third parties distorts incentives and could lead to a race to the bottom among 

vendors. 

If you have any further questions, please do not hesitate to contact me. Thank you for considering this 

comment, and I look forward to monitoring this proceeding carefully. 

Respectfully submitted,

Rohit Chopra 
Before the

DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT 

Washington, D.C. 20410 

 ) 

Proposed Rule to Amend HUD’s 

Interpretation of the Fair Housing Act’s 

Discriminatory Effects Standard 

 

) 

) 

) Docket No. FR-6111-P-02

) 

) 

 ) 

COMMENT OF

FTC COMMISSIONER ROHIT CHOPRA*

I write to outline concerns with the Department of Housing and Urban Development’s (HUD) 

proposed rule amending the Fair Housing Act’s discriminatory effects standard, sometimes 

referred to as the disparate impact rule. 

By way of background, I serve as a Commissioner of the Federal Trade Commission (FTC), 

which has significant interest in the application of discriminatory effects standards, as well as 

policies related to algorithmic bias. First, the FTC is the primary federal enforcement agency on 

competition, privacy, and data security issues, which are cross-cutting concerns when 

considering the widespread adoption and use of predictive analytics powered by algorithms. 

Second, the FTC has the authority to enforce the Equal Credit Opportunity Act (ECOA) and 

Regulation B, and the agency has been a member of an Interagency Task Force on Fair Lending. 

The FTC has also published relevant research reports, such as those related to data brokers and 

facial recognition. More recently, the FTC convened a public hearing focused on how bias and 

discrimination could impact the use of algorithms, artificial intelligence, and predictive analytics 

in business decisions and conduct. Third, we have taken enforcement actions against companies 

for deploying algorithms in ways that resulted in legal violations. In one such case, the FTC 

charged RealPage, Inc., for violating the Fair Credit Reporting Act (FCRA) by using an 

algorithm that resulted in major accuracy issues with the tenant screening information provided 

to its clients.1

 

 

* This comment represents my own views and does not necessarily reflect those of the Federal Trade Commission or 

any other Commissioner.

1 Press Release, Fed. Trade Comm’n, Texas Company Will Pay $3 million to Settle FTC Charges That it Failed to 

Meet Accuracy Requirements for its Tenant Screening Reports (Oct. 16, 2018), https://www.ftc.gov/newsevents/press-releases/2018/10/texas-company-will-pay-3-million-settle-ftc-charges-it-failed. 
2

I previously served as an assistant director at the Consumer Financial Protection Bureau, which 

also enforced ECOA and Regulation B. Prior to my government service, I gained experience by 

analyzing discriminatory effects of online peer-to-peer lending. 

Discrimination is a silent pickpocket. It illegally and unfairly robs people of economic gains 

based solely on certain aspects of their private identity, often without their knowledge. Whether 

it is a hidden tax imposed by inflated rates and rents or an invisible wealth drain caused by lost 

economic opportunities, discrimination carries an enormous economic cost and requires vigorous 

policing. 

Discrimination can be tough to detect and challenging to prove because it often occurs when 

practices, policies, or systems that seem neutral produce discriminatory results. The appearance 

of neutrality does not mean that the impact was unintended, nor does it absolve the disparate 

outcome. This is why courts have recognized that it is appropriate for law enforcement to focus 

on such effects. 

This effects-based standard, also known as the disparate impact standard, has long been used to 

fight housing discrimination under the Fair Housing Act. The Act prohibits discrimination based 

on race, color, national origin, religion, sex, familial status, or disability when renting or buying 

a home, getting a mortgage, seeking housing assistance, or engaging in other housing-related 

activities.2

The disparate impact standard for fair housing was codified in 2013 when HUD finalized a rule 

conferring liability for practices that actually or predictably result in a disparate impact, even if 

the discrimination is unintended.3 Anti-discrimination laws in other areas, including those in 

employment and credit, have also relied on effects-based standards. To legally justify a practice 

with discriminatory effects, HUD’s rule laid out a burden-shifting framework that requires 

defendants to provide substantial evidence that the practice is serving a legitimate business 

purpose. In addition, the framework imposes liability when a less discriminatory alternative is 

available. 

HUD’s latest proposed rule would gut these protections in ways that should be well understood 

by the agency. In the Fair Housing Act complaint that it filed against Facebook this past March, 

HUD convincingly identified the ways in which algorithms like the ones at issue in the proposed 

rule facilitate discrimination against protected groups.4 The safe harbors that it is proposing

would make it too difficult for victims to bring claims and would give defendants multiple ways 

to justify using algorithms with a discriminatory effect to achieve “legitimate objectives.”5 

 2 Housing Discrimination under the Fair Housing Act, DEP’T. OF HOUSING AND URB. DEV., 

https://www.hud.gov/program_offices/fair_housing_equal_opp/fair_housing_act_overview (last visited Oct. 11, 

2019).

3 Implementation of the Fair Housing Act’s Discriminatory Effects Standard, 78 Fed. Reg. 11460, 11482 (Feb. 15, 

2013) (codified at 24 C.F.R. pt. 100), 

https://www.hud.gov/sites/documents/DISCRIMINATORYEFFECTRULE.PDF. 4 Charge of Discrimination, FHEO No. 01-18-0323-8. 5 HUD’s Implementation of the Fair Housing Act’s Disparate Impact Standard, 84 Fed. Reg. 42854, 42858 

(proposed Aug. 19, 2019) (to be codified at 24 C.F.R. pt. 100), https://www.govinfo.gov/content/pkg/FR-2019-08-

19/pdf/2019-17542.pdf. 
3

First, alleged perpetrators could identity all the inputs used by the algorithm and show (a) how 

the inputs are not substitutes for any protected class and (b) how the model is predictive of risk 

or another “valid objective.” Second, they could show that a third party created or maintains the 

algorithmic model. Or third, they could show that a neutral third party has analyzed the model 

and found it to be empirically derived, with no proxy inputs, and predictive of risk or other valid 

objectives. Among the objectives HUD appears to consider valid is enabling “profit-related 

decisions.”6 If enacted, this proposal would give immunity to companies and individuals when 

they discriminate against tenants using algorithmic tools. 

My comment outlines three arguments against HUD’s proposed changes to the interpretation of 

the discriminatory effects standard. First, algorithms are not neutral, and even valid inputs can 

produce discriminatory results. Second, it is inappropriate to create safe harbors around

technologies that are proprietary, opaque, and rapidly evolving. Finally, outsourcing liability for

algorithmic discrimination to third parties distorts incentives and could lead to a race to the 

bottom among vendors.

Neutral Inputs Are Not Neutral Outputs

HUD’s proposed rule appears to fundamentally misunderstand how algorithms, big data, and 

machine learning work in practice. The proposed safe harbors rest on the false assumption that it 

is possible for an algorithm to operate free from bias and that neutral inputs produce neutral 

outputs. Overwhelming empirical evidence and HUD’s own experience prove this is a fallacy. 

The legal violations that HUD alleges in the complaint against Facebook provide an important 

window into the bias that is baked into algorithms. The complaint alleges that the design of

Facebook’s online behavioral advertising platform leads to biased results that discriminate 

against people from protected classes. As the complaint notes, Facebook “collects millions of 

data points about its users, draws inferences about each user based on this data, and then charges 

advertisers for the ability to microtarget ads” based on those inferences.7 This is a system built to

selectively target and exclude people based on predictions about their behavior, surmised by 

closely tracking the details of their daily life and those they interact with. 

Algorithms make predictions based on the universe of available data points. These predictions 

are then used to make decisions like whom to advertise to, whom to provide credit to, whom to 

select as tenants, and what to charge. Companies cannot use protected class statuses such as race, 

gender, or religion as factors to predict risk or make business decisions. But keeping data about

protected characteristics out of an algorithm is not enough to prevent it from producing 

discriminatory predictions. That’s because other attributes can serve as a substitute or proxy for 

protected class when used as algorithmic inputs. 

 6 84 Fed. Reg. 42854, 42855.

7 Charge of Discrimination ¶ 7.
4

These inputs do not have to be intuitive stand-ins to result in discrimination.

8 Seemingly 

“neutral” inputs, especially when analyzed in combination with other data points, can also be a 

substitute. Members of a protected class will likely have a wide range of other characteristics in 

common that can be detected with the increased collection of more and different types of 

information. Amassing a long list of personal details about each member of a large population 

makes it easier to calculate correlations, patterns, and dependencies among groups of people.

9

With more data points and more volume, any input or combination of inputs can turn into a 

substitute or proxy for a protected class. 

HUD’s Facebook complaint clearly outlines how “neutral” inputs can produce pernicious 

discrimination. According to HUD, Facebook uses “machine learning and other prediction 

techniques to classify and group users”10 based on “the data it has about that user, the data it has 

about other users whom it considers to resemble that user, and the data it has about ‘friends’ and 

other associates of that user.”11 Facebook’s data about users includes “which pages a user visits, 

which apps a user has, where a user goes during the day, and the purchases a user makes on and 

offline.”12

In using this data to classify users, HUD notes that Facebook “inevitably recreates groupings 

defined by their protected class. For example, the top Facebook pages users ‘like’ vary sharply 

by their protected class.”13 These discriminatory classifications not only decide who will see ads. 

They also set the prices advertisers will pay to show the same ad to different users. Therefore,

as HUD points out, “by grouping users who 'like’ similar pages (unrelated to housing) and 

presuming a shared interest or disinterest in housing-related advertisements, [Facebook’s] 

mechanisms function just like an advertiser who intentionally targets or excludes users based on 

their protected class.”14 That is, “neutral” inputs such as “likes” on Facebook can serve as a 

proxy for a protected class, giving landlords a green light to discriminate. 

The discrimination HUD alleges on Facebook’s platform is only likely to worsen. The rise of 

mass surveillance is turning each individual’s every movement, communication, connection, and 

creation into a data point inputted into algorithms. As HUD’s complaint points out, Facebook 

alone is collecting millions of data points about each user, most of them passively or 

unknowingly provided. The HUD complaint describes how “users may disclose some data about 

themselves when they set up their profiles, such as name and gender” but disclose most data 

 8 In a recent article on the risks posed by modern algorithms, Prince and Schwarcz call this phenomenon “proxy 

discrimination.” They explain how artificial intelligence (AI) is “inherently structured to engage in proxy 

discrimination whenever they are deprived of predictive data. Simply denying AIs access to the most intuitive 

proxies for predictive variables does nothing to alter this process; instead it simply causes AIs to locate less intuitive 

proxies.” See Anya Prince & Daniel Schwarcz, Preliminary Draft, Proxy Discrimination in the Age of Artificial 

Intelligence and Big Data, IOWA L. J. (forthcoming 2020), https://www.globaltort.com/2019/03/burgeoning-aiissues-schwarcz-daniel-b-and-prince-anya-proxy-discrimination-in-the-age-of-artificial-intelligence-and-big-datamarch-6-2019/. 9 Charge of Discrimination ¶¶ 16 – 20. 10 Id. ¶ 20. 11 Id. ¶ 17. 12 Id. ¶ 16. 13 Id. ¶ 20. 14 Id.
5

“unwittingly through the actions they, and those associated with them, take on and off of 

[Facebook’s] platforms.” 15

The sheer volume of data harvested about each individual exponentially expands the number of 

potential new substitutes and proxies for protected classes in both obvious and opaque ways. For 

example, geo-location tracking can identify an individual’s residence – a well-established proxy 

– but also other potential location proxies such as school, employer, doctor, house of worship, 

daycare, and the like. HUD should understand this as it was key to its lawsuit against Facebook. 

The HUD complaint quotes Facebook’s promotional pitch, which explains how the company

“use[s] location-related information-such as your current location, where you live, the places you 

like to go, and the businesses and people you’re near to provide, personalize and improve [their]

Products, including ads, for you and others.”16

Unless or until the government steps in to curtail online surveillance, the number of data inputs 

will continue to rapidly expand as technology opens up new ways to track and record people’s 

private lives. It is operationally impractical to expect the courts to expend time and resources on 

the process of individually identifying and analyzing what is likely to be a massive and growing 

universe of inputs, particularly given the increasing likelihood that big data can turn almost any 

input into a proxy. As algorithms become more sophisticated and machine learning becomes 

smarter, the complexity of the calculations used to produce predictions will make it nearly 

impossible for even the developers to isolate the impact of any given input on discriminatory 

outcomes. 

Safe Harbors Should Be Public Not Proprietary

A safe harbor is supposed to function as a proven pathway for following the law. Safe harbors 

can be a reasonable tool when they provide industry with specific, public guidelines for 

compliance. Key to the success of these interventions is that the rules of the road are clear and 

readily verifiable: a company that fails to comply can be easily exposed by consumers, 

counterparties, and regulators. 

But safe harbors do not work if regulators have no visibility into the practice at issue. Algorithms 

are not only nonpublic, they are actually treated as proprietary trade secrets by many companies. 

Victims of discriminatory algorithms seldom if ever know they have been victimized. Even if 

they do and bring a lawsuit, creators of the algorithm are likely to fiercely resist public disclosure 

of their systems, reducing the lawsuit’s salutary effect on public awareness and corporate 

compliance. 

To make matters worse, machine learning means that algorithms can evolve in real time with no 

paper trail on the data, inputs, or equations used to develop a prediction. Given the rapid pace of 

evolution, it is plausible that even the creators of the algorithms may be unaware of how inputs 

interact with each other or which inputs are determinative in shaping the outcomes. This will 

make it all but impossible for regulators and the public to have visibility into broader patterns or 

problems in housing discrimination. 

 15 Id. ¶ 12. 16 Id. ¶ 7.
6 

It is useful to contrast this proposal with other safe harbors that HUD has implemented. In the 

1990s, HUD issued detailed guidelines for accessibility requirements in multifamily housing. It 

created a safe harbor for builders that followed the guidelines, which included “detailed 

illustrations and sample room designs.”17 This framework allowed HUD and the public to readily 

assess whether the law was being followed: building codes, unlike algorithmic codes, are not 

trade secrets.

The opacity and complexity of algorithmic decision-making heavily weigh against the proposed 

rule to allow companies to outsource their liability for discriminatory outcomes to third-party 

algorithms. It also strongly counsels against creating a safe harbor for point-in-time certification

by a third-party expert, given the rapid pace at which the model can change to make such

certification immaterial. We should be scrutinizing machine learning with caution and concern 

instead of allowing it to become a blanket shield for housing discrimination. 

Outsourcing Liability for Discrimination Creates a Race to the Bottom 

Shielding firms that purchase algorithms from third-party vendors creates a major loophole in 

civil rights protections by encouraging both lenders and vendors to ignore discriminatory 

outcomes. If landlords are shielded from liability, they have little incentive to vet vendors

carefully. The result of this dynamic is predictable: vendors will not compete on algorithmic

fairness, leading to a race to the bottom. 

Although the rule requires that vendors be “recognized,” this is not a defined term,18 and there 

does not exist any widely recognized “industry standards”19 by which to evaluate algorithmic 

fairness. The result is that most lenders will turn to other competitive features, especially price,

in choosing vendors, which only encourages vendors to forgo investing the resources in 

preventing discriminatory outcomes. When noncompliance carries no consequences, companies 

will not have the incentive to comply. 

Where there are discriminatory effects, companies and individuals would escape liability by 

pinning the blame on vendor algorithms, while vendors will claim that they were not the ones 

that made the final decision. 

The proposed rule moves enforcement against discrimination backwards and should not be 

finalized. 

17 Accessibility Requirements for Buildings, DEP’T OF HOUSING AND URB. DEV.,

https://www.hud.gov/program_offices/fair_housing_equal_opp/disabilities/accessibilityR (last visited Oct. 11, 

2019).

18 Bogus certifications is a problem across markets that the FTC has sought to tackle. See, e.g., Press Release, Fed. 

Trade Comm’n, FTC Sends Warning Letters about Green Certification Seals (Sept. 14, 2015), 

https://www.ftc.gov/news-events/press-releases/2015/09/ftc-sends-warning-letters-about-green-certification-seals. 19 84 Fed. Reg. 42854, 42862.