Office of Commissioner 

 Alvaro M. Bedoya 

UNITED STATES OF AMERICA 

Federal Trade Commission 

WASHINGTON, D.C. 20580 

Statement of Commissioner Alvaro M. Bedoya 

On FTC v. Rite Aid Corporation & Rite Aid Headquarters Corporation

Commission File No. 202-3190 

December 19, 2023 

1. Biased face surveillance hurts people. 

The Commission brings this action against Rite Aid over its use of face surveillance 

technology to “drive and keep persons of interest out of [its] stores.”1 The technology alerted 

employees when it identified a supposed match between a customer and the images in Rite Aid’s 

database.2

 The Commission charges that the technology produced thousands of incorrect 

matches.3

 We often talk about how surveillance “violates rights” and “invades privacy.” We should; 

it does. What cannot get lost in those conversations is the blunt fact that surveillance can hurt 

people. In its complaint, the Commission alleges: 

 A Rite Aid employee stopped and searched an 11-year-old girl because of a false match.4

The girl’s mother reported that she missed work because her daughter was so distraught 

about the incident.5 

 Rite Aid employees called the police on a customer because the technology generated an 

alert against an image that was later described as depicting “a white lady with blonde 

hair.”6 The customer was Black. 

 Many other customers – people shopping for food, medicine, and other basics – were 

wrongly searched, accused, and expelled from stores. Sometimes, they were humiliated 

in front of their bosses, coworkers, or families.7

Sadly, we’ve heard this all before. Two years ago, a mom in Detroit dropped her teenage 

daughter off at a local skating rink. But she couldn’t get in. A face recognition system used by 

that skating rink mistakenly blocked her entry. She’d never been there before. But a computer 

1

 Complaint for Permanent Injunction and Other Relief at para. 3, Fed. Trade Comm’n v. Rite Aid Corp. (E.D. Pa. 

Dec. 19, 2023). 

2 Id.

3 Id. at para. 31. 4 Id. at para. 91. 5 Id.

6 Id. at para. 48. 7 Id. at para. 4. 
2 

told security that she’d been in a fight there earlier that year – with 97% confidence.8

 In Detroit 

alone, at least three people have been falsely accused of a crime because of facial recognition 

technology.9

 Some have been arrested in front of their family.10 All have been Black.11

It has been clear for years that facial recognition systems can perform less effectively for 

people with darker skin and women.12 In spite of this, we allege that Rite Aid was more likely to 

deploy face surveillance in stores located in plurality-non-White areas than in other areas.13 Rite 

Aid then failed to take some of the most basic precautions. The Commission alleges Rite Aid 

gave a presentation that identified only a single risk with expanding its face surveillance 

program: “[m]edia attention and customer acceptance”;14 that it used low-quality images that 

were unsuitable for automated analysis, including those captured using closed-circuit TV 

cameras and media reports;15 that it trained store employees to “push for as many enrollments as 

possible”;16 and that it typically did not include confidence intervals to help store-level 

employees better gauge whether a match might be a false positive.17 

8

 Randy Wimbley & David Komer, Black teen kicked out of skating rink after facial recognition camera 

misidentified her, FOX 2 DETROIT (July 16, 2021), https://www fox2detroit.com/news/teen-kicked-out-ofskatingrink-after-facial-recognition-camera-misidentified-her. 9

 Kashmir Hill, Eight Months Pregnant and Arrested After False Facial Recognition Match, THE N.Y. TIMES (Aug. 

6, 2023), https://www nytimes.com/2023/08/06/business/facial-recognition-false-arrest.html. 10 Miriam Marini, Farmington Hills man sues Detroit police after facial recognition wrongly identifies him, 

DETROIT FREE PRESS (Apr. 13, 2021 4:07 PM), https://www.freep.com/story/news/local/michigan/2021/04/13/detroi

t-police-wrongful-arrest-faulty-facial-recognition/7207135002/. 11 Hill, supra note 9. 12 See, e.g., PATRICK GROTHER ET AL. NAT’L INST. OF STANDARDS AND TECHN., FACE RECOGNITION VENDOR TEST 

PART 3: DEMOGRAPHIC EFFECTS (2019), https://nvlpubs nist.gov/nistpubs/ir/2019/NIST.IR.8280.pdf (finding 

“empirical evidence for the existence of demographic differentials in the majority of contemporary face recognition 

algorithms that we evaluated.”); CYNTHIA M. COOK ET AL., DEMOGRAPHIC EFFECTS IN FACIAL RECOGNITION AND 

THEIR DEPENDENCE ON IMAGE ACQUISITION: AN EVALUATION OF ELEVEN COMMERCIAL SYSTEMS, IEEE

TRANSACTIONS ON BIOMETRICS, BEHAVIOR, AND IDENTITY SCIENCE (2019), https://mdtf.org/publications/demograp

hic-effects-image-acquisition.pdf (finding that demographics affected the confidence scores that algorithms assigned 

to a given match, with incorrect scores being more common for women, younger people, and subjects with darker 

skin); Krishnapriya K. S. et al., Characterizing the Variability in Face Recognition Accuracy Relative to Race

(Conference on Computer Vision and Pattern Recognition Workshops, 2019), https://openaccess.thecvf.com/content

CVPRW 2019/papers/BEFA/S Characterizing the Variability in Face Recognition Accuracy Relative to Rac

e CVPRW 2019 paper.pdf (finding algorithms produced higher confidence in true positive matches for men over 

women). See also Joy Buolamwini & Timnit Gebru, Gender Shades: Intersectional Accuracy Disparities in 

Commercial Gender Classification, 81 PROCEEDINGS OF MACHINE LEARNING RESEARCH 1, 8 (2018), 

https://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf (finding that all tested facial analysis tools 

designed to classify faces by gender perform better on male faces than female faces, better on lighter faces than 

darker faces, and worst on darker female faces). See also CLARE GARVIE, CENTER ON PRIVACY & TECHNOLOGY AT 

GEORGETOWN LAW, A FORENSIC WITHOUT THE SCIENCE: FACE RECOGNITION IN U.S. CRIMINAL INVESTIGATIONS 

21(2022), https://mcusercontent.com/672aa4fbde73b1a49df5cf61f/files/2c2dd6de-d325-335d-5d4e 

84066159df71/Forensic Without the Science Face Recognition in U.S. Criminal Investigations.pdf (surveying 

research and concluding that “[r]egardless of what differential is discovered, each of these studies underscores a key 

weakness in the reliability of a face recognition search—that the accuracy of a given search can change depending 

on the demographic of the person....”). 

13 Complaint, supra note 1, at para. 41. 14 Id. at para. 11. 15 Id. at para. 22. 16 Id. at para. 23. 17 Id. at para. 27. 
The result was a system that malfunctioned frequently and spectacularly.18 The problems 

were so obvious that employees expressed frustration about the accuracy of the system.19 Many 

people are frightened and frustrated by retail theft. But it is easy to see how biased surveillance 

systems like this one make stores less safe. 

People encountering these facts for the first time may reflect on the fact that “shopping 

while Black” was a problem long before the age of commercial face surveillance. A decade ago, 

then-President Barack Obama famously remarked that “[t]here are very few African-American 

men in this country who haven’t had the experience of being followed when they were shopping 

in a department store” – including him.20 

One key difference with face surveillance is that a computer is telling a person that the 

customer is suspicious. And people trust computers. This is in part because of “automation bias” 

– a human tendency to believe that what a machine tells us must be true.21

II. Today’s settlement offers a strong baseline for what an algorithmic fairness 

program should look like. 

 The attached Order has many strengths. First and foremost, it flat-out bans Rite Aid from 

using face surveillance for five years,

22 and requires deletion of biometric data collected in 

connection with the operation of the system.23 Going forward, if Rite Aid decides to take up such 

a system in the future, the Order: 

 forces Rite Aid to tell people when they are enrolled in the system, and tell them how to 

contest their entry into that system; 24

 forces Rite Aid to tell consumers when it takes some action against them based on that 

enrollment that could hurt them, and tells them how to contest those actions;25 

 forces Rite Aid to answer those complaints quickly;26

18 Id. at para. 31. 19 Id. 20 Gene Demby, Two Tales of Profiling, From The Highest Offices In The Land, NPR (July 19, 2013), 

https://www.npr.org/sections/codeswitch/2013/07/19/203715163/two-tales-of-profiling-from-the-highest-offices-inthe-land. 21 See, e.g., Kate Goddard et al., Automation bias: a systematic review of frequency, effect mediators, and 

mitigators, 19 J. OF THE AM. MED. INFORMATICS ASS’N 121, 121 (2017), https://www ncbi nlm nih.gov/pmc/article

s/PMC3240751/pdf/amiajnl-2011-000089.pdf. 22 Decision and Order at 6-7, Rite Aid Corporation, FTC Docket No. C-4308 (Dec. 19, 2023) (Provision I). 

23 Id. 

24 Id. at 13-15 (Provision IV) (this provision has a limited exception for situations where notice is impossible due to 

safety concerns or the nature of the security incident involved). 

25 Id. (this provision also has a limited exception for situations where notice is impossible due to safety concerns or 

the nature of the security incident involved). 

26 Id.

3 
4 

 requires robust testing, including testing for statistically significant bias on the basis of 

race, ethnicity, gender, sex, age, or disability – acting alone or in combination;

27

 requires detailed assessment of how inaccuracies may arise from training data, hardware 

issues, software issues, probe photos, and differences between training and deployment 

environments; 28

 requires ongoing annual testing “under conditions that materially replicate” conditions in 

which the system is deployed; 29

 requires that Rite Aid shut down the system if it cannot address the risks identified 

through this assessment and testing;30 and 

 creates an opportunity for the Commission to conduct ongoing compliance monitoring.31

Commission Staff worked hard to reach this agreement under unusual and complex 

circumstances. I applaud them for it. 

 Having recognized those strengths, I want to be clear about two things. First, I want 

industry to understand that this Order is a baseline for what a comprehensive algorithmic fairness 

program should look like. Beyond giving people notice, industry should carefully consider how 

and when people can be enrolled in an automated decision-making system, particularly when that 

system can substantially injure them. In the future, companies that violate the law when using 

these systems should be ready to accept the appointment of an independent assessor to ensure 

compliance. 

Second, no one should walk away from this settlement thinking that this Commission 

affirmatively supports the use of biometric surveillance in commercial settings. While I believe 

we have obtained the strongest possible settlement with the evidentiary record and legal posture 

with which we have been presented, there is a powerful policy argument that there are some 

decisions that should not be automated at all; many technologies should never be deployed in the 

first place. I urge legislators who want to see greater protections against biometric surveillance to 

write those protections into legislation and enact them into law. 

III. This case is part of a broader trend of algorithmic unfairness. 

It is my view that Section 5 of the FTC Act requires companies using technology to 

automate important decisions about people’s lives – decisions that could cause them substantial 

injury – to take reasonable measures to identify and prevent foreseeable harms. Importantly, 

these protections extend beyond face surveillance. Indeed, the harms uncovered in this 

27 Id. at 7-13 (Provision III). 28 Id. 29 Id. 30 Id. 31 Id. at 25-6, 27-8 (Provisions XIV & XVI). 
5 

investigation are part of a much broader trend of algorithmic unfairness – a trend in which new 

technologies amplify old harms. 

We see it in hiring when employers develop résumé screening models, trained on their 

predominantly male workforce, that spuriously reject women.32 We see it in housing when 

advertising platforms steer housing ads away from people based on their sex and estimated race 

or ethnicity.33 And we see it in credit when a lender’s pricing models charge applicants who 

attended a Historically Black College or University higher rates for refinancing a student loan 

than similarly situated applicants who did not.34

Algorithmic unfairness is pernicious. It hurts people invisibly and at scale. And it tends to 

harm people because of who they are, reifying patterns of discrimination deeply embedded in our 

nation’s history.35 Algorithmic unfairness hurts people who are already hurting. I am proud that 

this Commission is moving to confront it. 

32 Jeffrey Dastin, Insight - Amazon scraps secret AI recruiting tool that showed bias against women, REUTERS (Oct. 

10, 2018), https://www reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scrapssecret-airecruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G. 33 Press Release, Dep’t of Just., Justice Department and Meta Platforms Inc. Reach Key Agreement as They 

Implement Groundbreaking Resolution to Address Discriminatory Delivery of Housing Advertisements (Jan. 9, 

2023), https://justice.gov/opa/pr/justice-department-and-meta-platforms-inc-reach-key-agreement-they-implementgroundbreaking. 34 STUDENT BORROWER PROT. CTR., EDUCATIONAL REDLINING 10 (2020), https://protectborrowers.org/wpcontent/uploads/2020/02/Education-RedliningReport.pdf. 35 As this Commission has previously explained, “Practices that meet the factors of Section 5(n) are not insulated 

from the Commission’s oversight merely because they involve discriminatory conduct. [...] Section 5 does not wilt 

when Congress legislates.” See Chair Lina M. Khan, Comm’r Rebecca Kelly Slaughter, and Comm’r Alvaro M. 

Bedoya, Joint Statement In the Matter of Passport Auto Group (Oct. 19, 2022), 

https://www.ftc.gov/system/files/ftc gov/pdf/joint-statement-of-chair-lina-m.-khan-commissioner-rebecca-kellyslaughter-and-commissioner-alvaro-m.-bedoya-in-the-matter-of-passport-auto-group.pdf.