1 

Transparency, Trust, and Consumer Protection in a Complex World 

Keynote Address Before Coalition for Networked Information 

Fall 2015 Membership Meeting 

December 15, 2015 

Good afternoon. Thank you, Clifford, for your warm introduction. And thank you to the 

Coalition for Networked Information (CNI) for inviting me to address your Fall 2015 

Membership Meeting. The vast array of topics that you have covered over the past two days is 

truly inspiring. This scope highlights the profoundly beneficial effects that digital technologies 

can have on access to information for research, education, cultural preservation, and other 

scholarly endeavors. I greatly admire the work that CNI has done over the years to connect 

researchers and educational efforts, and its ongoing efforts to make scholarship more accessible 

and collaborative. 

At first glance, CNI and my agency, the Federal Trade Commission (FTC), would seem 

to be focused on quite different issues. Yet I think it is clear that CNI and the FTC are wrestling 

with many of the same questions about the benefits and risks of an increasingly connected and 

data-driven society. One broad issue in which we share an interest is the Internet of Things. We 

are connecting nearly everything – from cars and buildings to clothing and light bulbs – to the 

Internet. Network equipment manufacturer Cisco reports that there are 25 billion networked 

devices in the world today and predicts that there will be 50 billion by 2020.1

 These sensors, 

along with our smartphones, tablets, and computers, generate twice as much data today as they 

did two years ago, and this trend is expected to continue. Sensors that are so small and efficient 

that they can power themselves with ambient radio waves are becoming a reality.2 But the 

number of connected devices and the relentless accumulation of data are only part of the story. 

The other part of the story is big data. Data is becoming cheaper to collect and keep, and 

our ability to analyze it is improving. This development holds many promises. Cities can better 

maintain their infrastructures by developing sophisticated early warning systems for gas and 

water leaks. Medical researchers can enroll patients in large-scale research projects and collect 

streams of useful data that, in the past, would have been a mere trickle coming from surveys and 

patients’ own reports.3

 In classrooms from pre-schools to universities, online learning resources 

are enriching students’ experiences and helping teachers tailor their instruction for students’ 

abilities and interests. And online courses – and the communities of students that grow up 

 1

 DAVE EVANS, CISCO INTERNET BUS. SOLUTIONS GRP., THE INTERNET OF THINGS: HOW THE NEXT EVOLUTION 

OF THE INTERNET IS CHANGING EVERYTHING 3 (2011), available at

http://www.cisco.com/web/about/ac79/docs/innov/IoT_IBSG_0411FINAL.pdf. These estimates include all types of 

connected devices, not just those aimed at the consumer market. 

2

 BBC News, Tiny Chip That Powers Itself from Radio Waves (Dec. 8, 2015), available at

http://www.bbc.com/news/technology-35038430. 

3 See, e.g., Elizabeth Whitman, Apple ResearchKit: Is New Open-Source Software for Sales or the Greater 

Good of Health Care, INTL. BUS. TIMES (Mar. 16, 2015 3:51 PM), available at http://www.ibtimes.com/appleresearchkit-new-open-source-software-sales-or-greater-good-health-care-1848612. 
2 

around them – are making it possible for people all over the world to learn and earn degrees from 

the world’s leading experts and most prestigious institutions.4

Some significant risks go along with the potential benefits of connected devices and big 

data. As we add devices to our homes, classrooms, and clothes, much more sensitive data will 

be collected. User interfaces on devices will shrink or disappear, making it more difficult for 

consumers to know when data is being collected, or to exercise any control. In fact, I expect that 

the Internet itself will soon “disappear” because connectivity will just be part of how things 

work, as electricity is today.5

 

These developments pose difficult challenges for privacy, security, and fairness in our 

society. In sensitive settings these challenges are particularly acute, such as education – where 

privacy violations and security breaches can cause a wide range of harms, and inaccurate or 

unfair data processing can have big ripple effects in students’ lives. More generally, the data that 

will be available as a result of these connected devices will be deeply personal, and big data 

analytics will make the data more readily actionable. Some of these devices will handle deeply 

sensitive information about our health, our homes, and our families. Some will be linked to our 

financial accounts, some to our email accounts. And devices themselves will be more closely 

connected with our actions in the physical world, making data security and device security 

critically important. 

But some fundamental aspects of our world will not change, no matter how connected 

and data-driven we become. Most importantly, we as individuals will remain roughly the same. 

We will not suddenly become capable of keeping track of dozens or hundreds of streams of our 

data, peering into the depths of algorithmic decision-making engines, or spotting security flaws 

in the countless devices and pieces of software that will surround us. Faced with a world of 

uncertainty about which devices are safe and whether consumers are getting a fair shake in the 

big data world, consumers could use some help. 

I am optimistic that consumers will be able to navigate and benefit from this complex, 

uncertain, and exciting world. The key, I think, is keeping our focus on the over-arching value 

of trust. Trust is the flip side of risk. It is the “expectation of favorable reciprocity from others 

in situations that are uncertain or risky.”6

 Trust does not depend on knowing everything there is 

to know about the person, company, or thing with which you’re interacting. Instead, trust 

becomes an expectation, in which we ask: Will this person, company, or device do what I expect 

it to do – and not something else?7

 So there is a close connection between trust and 

 4 See, e.g., Madeleine Parker and Sarah Rockwood, UC Berkeley Offers New Online Data Science Master’s 

Degree, The Daily Californian (last updated June 24, 2014), available at http://www.dailycal.org/2014/06/24/ucberkeley-offers-new-online-data-science-masters-degree/. 

5

 Chris Matyszczyck, The Internet Will Vanish, Says Google’s Eric Schmidt, CNET (Jan. 22, 2015, 6:00 PM), 

available at http://www.cnet.com/news/the-internet-will-vanish-says-googles-schmidt/. 

6

 Ashwin Mathew and Coey Cheshire, The New Cartographers: Trust and Social Order Within the Internet 

Infrastructure 6 (Aug. 15, 2010), available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1988216 (citations 

omitted). 

7 See National Academy of Sciences, TRUST IN CYBERSPACE (ed. Fred B. Schneider) (1999) (discussing trust in 

the context of IT systems). 
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transparency. And it is this connection between trust and transparency in the context of the 

Internet of Things and big data that I would like to discuss today. 

The FTC’s Role in Protecting Consumers’ Privacy and Data Security 

Before I get to that discussion, let me first describe the role that the FTC plays in privacy, 

data security, and consumer protection in general. We are the nation’s leading consumer 

protection agency, and we share competition enforcement with the Department of Justice. Under 

authority given to us in Section 5 of the FTC Act, the FTC is responsible for protecting 

consumers from a broad range of “unfair or deceptive acts or practices.”8

 Under this Section 5 

authority, the FTC has brought nearly 100 privacy and data security enforcement actions. The 

flexibility of Section 5 and our broad authority to obtain remedies that protect consumers have 

allowed us to keep up with rapid changes in technology. For example, we have brought actions 

against companies for allegedly collecting information inappropriately from consumers’ mobile 

devices,9 making unwarranted intrusions into private spaces,10 exposing health and other 

sensitive information, exposing previously confidential information about individuals’ networks 

of friends and acquaintances,11 and providing sensitive information to third parties who in turn 

victimize consumers.12 We have also brought hundreds of cases vindicating consumers’ rights 

under laws that protect sensitive information about children,13 financial information,14 medical 

data,15 and information used to make decisions about consumers’ credit, insurance, employment 

and housing.16

The FTC also maintains a busy policy docket. At the beginning of this year, we 

published a report on the Internet of Things, which emphasizes the importance of data and device 

security as well as the applicability of established privacy principles to connected devices.17 

 8

 15 U.S.C. § 45(a). 

9 See, e.g., Goldenshores Techs. LLC C-4466 (F.T.C. Mar. 31, 2014) (decision and order), available at

https://www.ftc.gov/system/files/documents/cases/140409goldenshoresdo.pdf. 

10 See FTC, Press Release, Aaron’s Rent-To-Own Chain Settles FTC Charges That It Enabled Computer Spying 

by Franchisees (Oct. 22, 2013), available at https://www.ftc.gov/news-events/press-releases/2013/10/aarons-rentown-chain-settles-ftc-charges-it-enabled-computer. 

11 See Facebook, Inc., C-4365 (F.T.C. July 27, 2012) (decision and order), available at

https://www.ftc.gov/sites/default/files/documents/cases/2011/10/111024googlebuzzdo.pdf/ 

12 FTC v. Sitesearch Corp., d/b/a LeapLab (D. Az. Dec. 23, 2014) (complaint), available at

http://www.ftc.gov/systems/files/documents/cases/141223leaplabcmpt.pdf. 

13 See Children’s Online Privacy Protection Act, 15 U.S.C. §§ 6501-06. 

14 15 U.S.C. §§ 6801-09. 

15 Health Insurance Portability and Accountability Act, Pub. L. No.104-191, 110 Stat. 1936 (1996) (codified in 

scattered sections of 18, 26, 29, and 42 U.S.C.). 

16 15 U.S.C. § 1681 et seq. 

17 See generally FTC, INTERNET OF THINGS: PRIVACY & SECURITY IN A CONNECTED WORLD 1-4 (2015) (staff 

report), available at https://www.ftc.gov/system/files/documents/reports/federal-trade-commission-staff-reportnovember-2013-workshop-entitled-internet-things-privacy/150127iotrpt.pdf (discussing views of workshop 

participants) [IOT REPORT]. 
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Before that, we published a detailed study of the data broker industry,18 which was in the big 

data business long before the words “big data” became part of our policy lexicon. We also held 

public workshops on so-called alternative consumer scores19 and the potential for big data 

analytics to be used in ways that discriminate against consumers.20 And, just last month, we held 

a public workshop on cross-device tracking, which refers to companies’ efforts to correlate a 

consumer’s activities as she moves from smartphone to tablet to desktop computer.21 

Transparency, Privacy, and Trust 

So let me now turn to the connection between transparency, privacy and consumer trust. 

Consumers want to know – and should be able easily to find out – what information companies 

are collecting, where they’re sending it, and how they’re using it. This kind of information is 

important to consumers’ decisions about whether to use digital products and services in the first 

place. The FTC came to this realization early in the history of the commercial Internet.22 

But transparency is also important in situations in which consumers are not making 

choices about whether to interact with a company, but instead where the company is operating 

behind the scenes in ways that can significantly affect consumers. Data brokers illustrate this 

point well. Data brokers are companies that assemble individual profiles on consumers by 

collecting information from far-flung sources, but typically do not interact with consumers 

themselves. Through these profiles, consumers can end up in marketing segments drawn along 

lines of race, ethnicity, financial status, health conditions, and other sensitive characteristics. 

With all of this data, and the inferences that data brokers can draw from it, they put consumers 

into categories or segments that had labels such as “Financially Challenged”, “Bible Lifestyle”,23

“Diabetes Interest”24, “Metro Parents” (which are lists of single parents who are “primarily high 

school or vocationally educated” and are handling the “stresses of urban life on a small budget”), 

and “Timeless Traditions” (a list of immigrants who “speak[] some English, but generally 

 18 FTC, DATA BROKERS: A CALL FOR TRANSPARENCY AND ACCOUNTABILITY (2014), available at 

http://www.ftc.gov/system/files/documents/reports/data-brokers-call-transparency-accountability-report-federaltrade-commission-may-2014/140527databrokerreport.pdf [DATA BROKER REPORT]. 

19 See FTC, Spring Privacy Series: Alternative Scoring Products (Mar. 19, 2014), available at

https://www.ftc.gov/news-events/events-calendar/2014/03/spring-privacy-series-alternative-scoring-products. 

20 See FTC, Big Data: A Tool for Inclusion or Exclusion? (Sept. 15, 2014), available at

https://www.ftc.gov/news-events/events-calendar/2014/09/big-data-tool-inclusion-or-exclusion. 

21 See FTC, Cross Device Tracking: An FTC Workshop (Nov. 16, 2015), available at

https://www.ftc.gov/news-events/events-calendar/2015/11/cross-device-tracking. 

22 See, e.g., In re GeoCities, Inc., 127 F.T.C. 94 (1999) (consent order), available at

https://www.ftc.gov/enforcement/cases-proceedings/982-3015/geocities (settling charges that website had 

misrepresented the purposes for which it was collecting personally identifiable information from children and 

adults); FTC v. Toysmart.com, LLC, No. 00-11341-RGS, 2000 WL 34016434 (D. Mass. July 21, 2000) (consent 

order), available at https://www.ftc.gov/enforcement/cases-proceedings/x000075/toysmartcom-llc-toysmartcom-inc

(challenging website’s attempts to sell children’s personal information, despite a promise in its privacy policy that 

such information would never be disclosed). 

23 DATA BROKER REPORT, supra note 18, at 20 n.52, 21. 

24 Id. at 46, 55. 
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prefer[] Spanish”).25 These practices were recently brought to light by the FTC and others.26 

While consumers might benefit from some segmentation – by receiving more relevant 

advertising, for example – consumers should have choices about where their data ends up and 

how it is used. 

Transparency is enhanced by giving consumers “just in time” information, at key 

moments when it is most relevant to them, such as when they are deciding to download an app or 

make purchases on a connected device. But transparency should also include helping consumers 

navigate the complex ecosystem of data, devices, and big data analytics operating behind the 

scenes, so that consumers understand the practices that can affect them, and exercise choices 

about the practices. 

Think about the alternatives to being transparent with consumers. One reasonable 

response of consumers is that they will harbor suspicions about a product or service and may 

choose to avoid it, or use it less than they would if they trusted it fully. Another response – 

particularly when an entity that consumers don’t know about is collecting or using data – is to 

react angrily when the truth about the company’s data practices come out. And I think it’s wise 

to presume that the truth will come out eventually. In either case, the result is the same: 

Consumers or customers lose trust in a company, and the results for the company can be 

devastating. 

Many companies and organizations understand this important connection between 

transparency, privacy, and trust. But being transparent in this data-intensive age is challenging. 

With the Internet of Things, many connected devices do not have a user interface to present 

information to consumers about data collection. Devices are becoming more numerous, adding 

to the mountain of information that companies present to consumers in privacy policies. As 

devices become integrated into homes and other physical spaces, there are also questions around 

who should receive disclosures about data collection and use practices. How will the consumers 

who buy a device – and the innocent bystanders around them – know when a device is recording 

images or audio? And there are other questions, like how can consumers choose to avoid having 

their data collected? For how long will their data be kept by the companies who are collecting 

it? And how will these companies keep the data secure? 

Companies that provide connected devices should recognize that providing transparency 

will require some creative thinking. Visual and auditory cues, and immersive apps and websites 

should be employed to describe to consumers, in meaningful and relatively simple way, the 

nature of the information being collected. The same signals should be used to provide 

 25 Id. at 20 n.52. 

26 See, e.g., CBS News, 60 Minutes; The Data Brokers: Selling Your Personal Information (last updated Aug. 

24, 2014), available at http://www.cbsnews.com/news/data-brokers-selling-personal-information-60-minutes/; 

World Privacy Forum, The Scoring of America (2014), available at

http://www.worldprivacyforum.org/2014/04/wpf-report-the-scoring-of-america-how-secret-consumer-scoresthreaten-your-privacy-and-your-future/; U.S. Senate Committee on Commerce, Science, and Transportation, A 

Review of the Data Broker Industry: Collection, Use, and Sale of Consumer Data for Marketing Purposes (Dec. 

2013) (staff report), available at http://www.commerce.senate.gov/public/?a=Files.Serve&File_id=0D2B3642-

6221-4888-A631-08F2F255B577. 
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consumers with choices about whether any of this information can be used by entities or persons 

who fall outside the context in which the consumer is employing the device, and in which the 

consumer expects her information to remain private. 

Another promising tool for providing information to consumers, as well as allowing them 

to exercise meaningful choices, is the “command center” that companies are now developing to 

run multiple household connected devices.27 The driving force here is convenience, but these 

command centers could also provide an opportunity for consumers to understand the information 

their devices are generating, and to control where that information goes. After all, if you can 

have a centralized interface to program your garage door, thermostat, television, refrigerator, and 

who knows what else, you ought to be able to use that same interface to make meaningful 

choices about the data your devices will collect and where they will send it. 

Transparency, Fairness, and Trust 

Now let me turn to the issue of transparency and fairness in big data analytics and how it 

relates to consumer trust. Big data analytics, employed both by data brokers and within 

companies themselves, are being used to segment consumers by interests and traits and to make 

an increasingly wide range of decisions about them. Some of these analytics projects could 

create questions about fairness. For example, a company might analyze its own data in an effort 

to identify “good” versus “troublesome” customers. But what if this analysis ends up tracking 

individuals along racial or ethnic lines. A Harvard Business Review article argues that this kind 

of result isn’t just possible, but inevitable.28 

Transparency for big data analytics – both within companies and through third parties –is 

necessary to engender trust by informing consumers about the significant impact that big data 

analytics can have on them and clarifying their choices with respect to some kinds of data 

collection and use. Transparency is also a helpful check on potentially troublesome data 

practices, since as Louis Brandeis famously said, “sunlight is said to be the best of 

disinfectants.”29 Without more transparency in big data analytics, questions will linger about the 

role that big data analytics plays in the marketplace and whether consumers are being treated 

fairly. But the question is how to present information that is meaningful to consumers. 

Many of these kinds of decisions are based on some kind of score – a number generated 

by an algorithm that gives some indication of what a consumer is likely to be interested in or 

how she is likely to behave. A familiar example is the credit score. Credit scores are basically 

predictions of how likely a consumer is to repay a debt. The higher the score, the lower the 

credit risk. In their early days, credit scores were used strictly for credit decisions – whether you 

 27 See Don Clark, The Race to Build Command Centers for Smart Homes, WALL ST. J. (Jan. 5, 2015), available 

at http://www.wsj.com/articles/the-race-to-build-command-centers-for-smart-homes-1420399511. 

28 See Michael Schrage, Big Data’s Dangerous New Era of Discrimination, HARVARD BUSINESS REVIEW BLOG 

NETWORK (Jan. 29, 2014, 8:00 a.m.), available at http://blogs.hbr.org/2014/01/big-datas-dangerous-new-era-ofdiscrimination/.

29 Brandeis Univ., Louis D. Brandeis Legacy Fund for Social Justice, available at

http://www.brandeis.edu/legacyfund/bio.html (last visited Dec. 14, 2015). 
7 

would qualify for a mortgage, for example, and the interest rate that you would be offered. Over 

time, the use of these same credit scores expanded to other major decisions about consumers, 

such as whether a prospective employer would extend a job offer to an applicant, or an insurance 

company would charge a higher premium on auto or homeowners insurance. 

We know a great deal about what information is in our credit reports and what our 

traditional credit scores are for a simple reason. Congress has required some transparency in 

credit scoring. In 2003, Congress instructed the FTC and the Federal Reserve to study whether 

one type of popular credit score used for auto insurance employed factors that serve as proxies 

for race, gender, or other traits that could give rise to unlawful discrimination.30 In addition, 

Congress required credit bureaus to make consumers’ credit reports available to them for free,31

and credit scores are increasingly becoming available for free to consumers.32 

These transparency requirements and practices have been good for consumers, credit 

bureaus, and companies that rely on credit scores to make business decisions.33 With the 

transparency provided by free credit reports and, increasingly, scores, consumers can more 

effectively exercise their rights to dispute and correct inaccurate information. And the thorough 

analysis of one critical type of credit score by the FTC and Federal Reserve made users more 

confident that this score was not discriminatory. 

Today, we’re seeing a proliferation of other types of scores being used to make eligibility 

determinations covered by the Fair Credit Reporting Act.34 While these scores are subject to the 

same obligations of access, accuracy, security, and other requirements imposed by the FCRA, 

they haven’t yet been subject to the same kind of scrutiny that Congress and the federal agencies 

brought to bear on traditional credit scores.35 The use of new sources of information, including 

 30 See Fair and Accurate Credit Transactions Act of 2003 § 215, Pub. L. 108-159 (Nov. 5, 2003), available at

https://www.congress.gov/bill/108th-congress/house-bill/2622/text [“FACTA”]. 

31 See FACTA, supra note 30, at § 211 (codified at 15 U.S.C. § 1681j(a)). 

32 See Annamaria Andriotis, Millions of Consumers to Gain Access to Credit Scores, WALL ST. J. TOTAL 

RETURN (Oct. 17, 2014 2:38 PM), available at http://blogs.wsj.com/totalreturn/2014/10/17/millions-of-consumersto-gain-access-to-credit-scores/ (reporting that some credit card issuers are reporting consumers’ credit scores on 

their monthly statements). 

33 See BOARD OF GOVERNORS OF THE FEDERAL RESERVE SYSTEM, REPORT TO CONGRESS ON CREDIT SCORING 

AND ITS EFFECTS ON THE AVAILABILITY AND AFFORDABILITY OF CREDIT S-1 (Aug. 2007), available at 

http://www.federalreserve.gov/boarddocs/rptcongress/creditscore/creditscore.pdf (“The large savings in cost and 

time that have accompanied the use of credit scoring are generally believed to have increased access to credit, 

promoted competition, and improved market efficiency.”) [FRB, CREDIT SCORING REPORT]. 

34 See generally FTC, Transcript of Spring Privacy Series: Alternative Scoring Products (Mar. 19, 2014), 

available at http://www.ftc.gov/system/files/documents/public_events/182261/alternative-scoring-products_finaltranscript.pdf; Pam Dixon and Robert Gellman, The Scoring of America: How Secret Consumer Scores Threaten 

Your Privacy and Your Future (Apr. 2014), available at http://www.worldprivacyforum.org/wpcontent/uploads/2014/04/WPF_Scoring_of_America_April2014_fs.pdf. 

35 See generally FRB, CREDIT SCORING REPORT, supra note 33; FTC, CREDIT-BASED INSURANCE SCORES: 

IMPACTS ON CONSUMERS OF AUTOMOBILE INSURANCE 62-73 (July 2007), available at

https://www.ftc.gov/sites/default/files/documents/reports/credit-based-insurance-scores-impacts-consumersautomobile-insurance-report-congress-federal-trade/p044804facta_report_credit-based_insurance_scores.pdf. 
8 

information that goes beyond traditional credit files, to score consumers raises fresh questions 

about whether these alternative scores may have disparate impacts along racial, ethnic, or other 

lines that the law protects. 

And an increasing range of algorithmic scores and decisions fall outside of the 

framework provided by the Fair Credit Reporting Act. The FTC identified a few of them in our 

May 2014 report on data brokers. We highlighted so-called “risk mitigation” services as sources 

of potentially significant decisions about consumers that are not subject to the specific 

protections of the FCRA.36 These services answer questions like “Is this consumer who she 

claims to be?” and “Is the purchase that this consumer is attempting to make likely to be 

fraudulent?” While some uses of these “risk mitigation” scores may fall under the FCRA, an 

important set of them does not. 

Transparency is important across the full range of decisions that I’ve illustrated. But it’s 

not realistic to rely on the approach that the FTC took to understand one type of score used for 

auto insurance to gain an understanding of the full spectrum of scoring models used today. It 

took the FTC nearly four years to conduct its study. The FTC – and all other federal agencies for 

that matter – simply do not have the capacity to study every score out there. This approach 

simply will not scale. 

Moreover, scoring algorithms and other forms of big data analytics rely on statistical 

models and data system designs that few on the outside understand in detail. And even if we on 

the outside could peer into the hundreds of scoring algorithms that could potentially affect 

consumers, what would we learn? We might learn which features of a data set are used in a 

given algorithm, and what weight a company attaches to them. These details might be so 

abstract, and so rapidly changing, that they do not tell government, consumers or other 

concerned stakeholders much at all about what really matters – which is how the algorithms are 

actually used and whether they have discriminatory or other inappropriate effects. 

This suggests that testing the effects of big data analytics may be a promising way to go. 

This route, too, presents some challenges. On a technical level, many companies will not have 

data to answer definitively the question of whether they are treating consumers of different races 

or ethnicities differently. For example, an ad network might track consumers by using an email 

address or a device identifier. The ad network might be able to combine this information with 

other data that is readily available to it, such as which apps a consumer uses, to make inferences 

about more sensitive personal characteristics. But this is far different from having test data in 

which consumers’ race, health conditions, financial status, or other sensitive personal 

characteristics are known. 

Doing this kind of analysis from the outside is difficult. Researchers have done some 

proof-of-concept studies, but they required considerable work and involved efforts to tackle 

some cutting-edge research questions.37

 36 See DATA BROKER REPORT, supra note 18, at 32-34. 

37 See, e.g., Amit Datta, Michael Carl Tschantz, and Anupam Datta, Automated Experiments on Ad Settings: A 

Tale of Opacity, Choice, and Discrimination, in PROC. ON PRIVACY ENHANCING TECHS. (PETS) 2015, available at

http://www.andrew.cmu.edu/user/danupam/dtd-pets15.pdf; Latanya Sweeney, Discrimination in Online Ad 
9 

This means that companies using scoring models should themselves do more to 

determine whether their own data analytics result in unfair, unethical, or discriminatory effects 

on consumers. In addition to scrutinizing their own practices, companies can provide consumers 

with creative UIs to give consumers more meaningful, usable access to their data. 

Ultimately, I believe we need legislation to address many of these issues. Technologists 

have a key role to play, too. They have the skills to make data access tools that are easy for 

consumers to use, and they have the technical insights that are necessary to determine whether 

specific analytics practices pose risks of excluding, or otherwise placing at a disadvantage, 

groups defined according to sensitive traits. I am hopeful that companies will give technologists, 

including designers and user interface experts, the support and resources needed to tackle these 

critically important challenges. 

* * * * * * 

Although I have addressed transparency, trust, privacy, and fairness from the consumer 

perspective, I hope that the challenges of the Internet of Things and big data that I’ve discussed – 

as well as my suggested solutions – apply to the schools and institutions in which many of you 

work. Trust is every bit as important to students, parents, and researchers in the educational and 

research context as it is to consumers in the commercial marketplace. Despite the complexity of 

some of these problems, some simple questions and operating principles can help guide all of us. 

The more you can tell your stakeholders about what you’re doing to keep their data secure, to use 

it fairly, and generally to meet their expectations for the kinds of sensitive data they you collect, 

store, and use, the better off everyone will be. 

Thank you. 

 Delivery, 56 COMMS. OF THE ACM, No. 5, at 44-54 (2013), available at

http://cacm.acm.org/magazines/2013/5/163753-discrimination-in-online-ad-delivery/fulltext.