Big Data: A Tool for Inclusion or Exclusion?

Transcription

1 September 15 th Constitution Center th St SW, Washington DC Big Data: A Tool for Inclusion or Exclusion? Conference Description: The Federal Trade Commission will host a public workshop entitled Big Data: A Tool for Inclusion or Exclusion? in Washington on September 15, 2014, to further explore the use of big data and its impact on American consumers, including low income and underserved consumers. A growing number of companies are increasingly using big data analytics techniques to categorize consumers and make predictions about their behavior, said FTC Chairwoman Edith Ramirez. As part of the FTC s ongoing work to shed light on the full scope of big data practices, our workshop will examine the potentially positive and negative effects of big data on low income and underserved populations. The proliferation of smartphones, social networks, cloud computing, and more powerful predictive analytic techniques have enabled the collection, analysis, use, and storage of data in a way that was not possible just a few years ago. Tremendous benefits flow from the insights of big data, such as advances in medicine, education, and transportation, improved product offerings, more efficient manufacturing processes, and more effectively tailored advertisements. At the same time, concerns have been raised about whether big data may be used to categorize consumers in ways that may affect them unfairly, or even unlawfully. Companies such as financial institutions, online and brick and mortar retailers, lead generators, and service providers may use big data in the following ways: To reward loyal customers with better customer service or shorter wait times. To offer different prices or discounts to different consumers. For example, a financial institution may offer a consumer a discounted mortgage rate if that consumer has a checking, savings, credit card, and retirement account with a competitor. To tailor advertising for financial products. For example, high-income consumers may receive offers for gold level credit cards and low-income consumers may receive offers for subprime credit cards. To assess credit risks of particular populations. For example, some commentators have highlighted the use of unregulated aggregate scoring models that assess credit risks, not based on the credit characteristics of individual consumers, but on the aggregate credit characteristics of groups of consumers who shop at certain stores. Such uses of big data are expected to create efficiencies, lower costs, and improve the ability of certain populations to find and access credit and other services. At the same time, these practices may have an unfair impact on other populations, limiting their access to higher quality products, services, or content. The workshop will address the following issues: How are organizations using big data to categorize consumers?

2 What benefits do consumers gain from these practices? Do these practices raise consumer protection concerns? What benefits do organizations gain from these practices? What are the social and economic impacts, both positive and negative, from the use of big data to categorize consumers? How do existing laws apply to such practices? Are there gaps in the legal framework? Are companies appropriately assessing the impact of big data practices on low income and underserved populations? Should additional measures be considered? The workshop will bring together academics, business and industry representatives, and consumer advocates and will be open to the public. Welcome Tiffany George, Senior Attorney, Division of Privacy & Identity Protection, FTC We re at a stage of information where data is influencing the way we look at the world, boosting efficiency. Disparities are exacerbated between lower income individuals Opening Remarks Edith Ramierz, Chairwoman, FTC Big data we will have the ability to share its development and outcome to benefit members of society and have it be an economic inclusion tool. Big data tools such as GPS, smartphones and other mobile devices generate an efficient ecosystem throughout the day. The collective cost of collecting and storing and processing information means companies can accumulate unlimited information and store indefinitely: using predictive analytics to learn a surprising amount. From this data you can unlock the immense. This data will allow you to make inferences about it users and could lead to discrimination among lower income members of society Discrimination by algorithm, there are now products beyond traditional credit scores that support or scope everything. It should be entitled to certain protections under FDRA. If the same company lowers my credit based on the score, these scores could be used to influence the opportunities of low income minority or other populations to get credit Big data could be used for disparate or discriminatory output for consumers Today: Explore how big data can include and exclude consumers from opportunities in the marketplace and shed light from industry consumer civil rights government. Panelists are speakers about access to big data and current practices in the private sector to ensure big data is a force for economic inclusions not exclusions In-depth look at the risk of big data to low income people

3 Develop a tool that combines data to predict when individuals and families are homeless. This is a government rather than a business use, but hope speakers provide examples about companies to educate and prevent those in low income populations. Where do we go from here? No clear path for navigating big data and the way it is advancing. Differential impact on vulnerable populations, we may not know what the best course ought to be, I believe we should discuss three objectives going forward Big data may be vital to local law, websites that promote background checks for employees Discussion about ethical obligation, as stored information detailing every facet of consumers lives Encourage businesses to guard against bias by researching impact on low income populations in designing their algorithms and predictive products PPT Presentation: Framing the Conversation: Solon Barcas, Ph.D., Postdoctoral Research Associate, Princeton University Center for Information Technology Policy Data Mining: Automate the process of discovering Data Mining as Discrimination By definition, data mining is always a form of statistical (and therefore seemingly rational) discrimination Point of data mining is to provide a rational basis upon which distinguish between individuals To reliably confer to the individual the qualities possessed by those who seem statistically similar How data can discrimination: Target variables: Determine how to solve the problem. Treats the target variable as a function of some other observed characteristics The art of data mining: the proper specification of the target variable is frequently not obvious The definition of the target variable is associated class labels will determine what data happens to find It is possible to parse the problem and define the target variable in such a way that protection Training Data: Data Mining is really a way to learn by example

4 The data function as examples are known as training data Data collection: Data mining especially sensitive b/c it aims to extract general rules from a particular set of examples. Data gathered for routine business purposes tend to lack the rigor of social scientific data collection Uncounted, Unaccounted, Discounted Data collection: Limiting future contact Skewed results may lead to decision procedures that limit the future contact companies have with specified groups, skewing further data Labeling Examples - reproduce past prejudice So long as prior decisions affected by some form of prejudice serve as examples of correctly rendered determinations, data mining will necessarily infer rules that exhibit same prejudice Data mining can turn the conscious prejudice or implicit bias of individual involved in previous decision making into a formalized rule Feature Selection the process of settling on the specific string of input variables, protected classes may find that they suffer a disproportionate cost of errors At what cost? Obtaining information that is sufficiently rich to draw precise distinctions can be expensive. Even marginal improvements in accuracy may come at significant practical costs and a less exacting encompassing analysis Proxies very same criteria that correctly sorts individuals according to their predicted profitability, for example, may also sort individuals according to class membership These discoveries reveal the simple fact of inequality, but they also reveal the fact that these are inequalities Masking Data mining could also breathe new life into traditional forms of international discrimination because decision Makes with prejudicial views can mask their intentions by exploiting each of the mechanisms enumerated above Unintentional discrimination is likely to be far more common that the kinds of discrimination for malice Unintentionality, exacerbate inequality, no ready answer

5 Panel 1: Assessing the Current Environment Panel examines the current uses of big data in a variety of contexts and how these uses impact consumers. Moderator: Katherine Armstrong, Senior Attorney Division of Privacy and Identity Protection, FTC Panelists: Kristin Amerling Chief Investigative Counsel and Director of Oversight U.S. Senate Committee on Commerce, Science and Transportation Danah Boyd Principal Researcher, Microsoft Research Research Assistant Professor, New York University Mallory Duncan Senior Vice President and General Counsel National Retail Federation Gene Gsell Senior Vice President, U.S. Retail & CPG SAS David Robinson Principal Robinson + Yu Joseph Turow Professor Annenberg School for Communication, University of Pennsylvania Look at big data from both sides technical phenomenon, social phenomenon allow us to make sense of the world around us which in many ways observes the complexity Big data not simply in its technical, but socioeconomically Big data fascinating trajectory partly because of the growth of big data in the online worked Determining harm from big data discriminatory harm Some would argue fair credit reporting act is mechanism in the credit context because it s doing the sorts of adverse actions. You ve provided a notice that the adverse action was a result of something in the credit report that you re given

6 We have to make qualitative difference when talking about credit insurance or education, we may have very different expectations Hope and prediction would be that they re going to be some practices that emerge probably a collaborate fashion outside legislative process Access to credit is simply a fundamental right in this country example: access to high ends men fashion is not we ought not to confuse the two in this discussion The kinds of products that we saw in our review of data involve marketing did go beyond products designed to promote most appropriate car people most interested in other things. Consumers based their financial and house status that includes people with medical conditions consumers may not be as happy to find that they can be targeted for the best care when that doesn t match their economic needs. Point where they report that good searches could find people and identify them with specifics about health and personal life that shouldn t be on the internet Parting remarks: I think it s shameful that senators ask a representative of the data industry whether he could name his clients, he refuses to do that. These are areas of life that impact us all, the collection of information about us and their use I think should be required. Companies should be required to say which data broker they use and what the categories are because they affect us. Educating people about credit card and loyalty programs obscure the idea of data is continuity, element of continuity between that and qualification of the individual. Trust and where things going today: unrealized opportunity to create greater trust with consumers in terms of how these technologies are being used Tools we have from prior regimes about notes from data collected, notice consent Don t have fine grain choice about what is going to happen We need to build new tools of accountability Using more positive than negative, lots of examples where big data solves problems, human problems, in all facets of live economic model still will drive most though process They want to be relevant and responsible. Privacy that things need to be monitored on balance consumers benefiting Consumers trust in that context more than just one element such as sharing this data flow companies out there that gather huge amounts of data consumers know this because they see feedback on websites appreciate that and go back and shop again because they trust amazon. FTC looking in terms of behaviors around them and think tis important to high light companies trying to figure out how to be responsible

7 Transparency and visibility the right to opt out if they don t want their info being used for marketing baseline for transparency o Interesting to hear about additional tools recognize complex and evolving issue and looking forward to continuing to be part of dialogue to impact big data and consumers Panel 2: What s on the Horizon with Big Data? This panel will explore potential uses of big data as well as the potential benefits and harms for particular populations of consumers Moderator: Tiffany George, Senior Attorney Division of Privacy and Identity Protection, FTC Panelists: Alessandro Acquisti Associate Professor of Information Systems and Public Policy Heinz College, Carnegie Mellon University and Co-director of the CMU Center for Behavioral Decision Research Pamela Dixon Founder and Executive Director World Privacy Forum Cynthia Dwork Distinguished Scientist Microsoft Research Mark MacCarthy Vice President for Public Policy Software Information Industry Association Stuart Pratt President and CEO, Consumer Data Industry Association Dr. Nicol Turner-Lee Vice President and Chief Research & Policy Officer Minority Media and Telecommunications Council Collection of examples where big data used to empowering people, advantages of using big data in many contexts examples How big data helps the underserved o Hospitals helping with medical insights and data could be available to help those in community clinics

8 o o Schools using predictive analytics to prevent kids from dropping out Reduce dropout rate significantly Socially beneficial purposes that increase socioeconomic standing What companies doing over the last few years? Allocation over the web what is the next big issue and what extent is that simply affecting the economic ways it s beneficial to everyone Cases privacy which can lead to economic growth because heath privacy legislation can promote innovation in the field of heath information exchanges, Promoting the growth of HIEs because it decreases privacy concerns and how to use the data Allocating effect evidence of privacy and lack of privacy affecting winners and losers Transfer of wealth from data subjects to elders Issue of transfer of wealth between different data subjects Role of personal information on social media More data, more furnaces equaling the remarks that market forces along weed out the bad from the good Ideal Trend: Big data being used to protect discrimination Find ways of countering discrimination analyze the way people behave and make suggestion to make their lives better previous panel defeated Scoring of America Big data is informative phase right now Root of matter is the moment that a person is put into a category in some way or is scored in some way, that triggers a data paradigm Big data used to help consumer and the exact data can be used to hurt consumers, data paradox, classification effect trigger this when classifying people have to do something about that in terms of fairness structure what do we do? How we solve problem of data paradox is what we need to do moving forward Trends in the future: Data going to get bigger and better tons of data, individual data that has an impact on social sciences Cisco 4.6 billion stake in internet makes data more important and valuable Data analytics can generate social and community benefit Big data can help us solve social problems, health problems, education problems, environmental problems potential for big data to help preserve income for people economically depressed

9 Data must be protected in such a way because a lot of times minority people hold on to identity tightly Think carefully about the value of innovation while at the same time for minority populations, big data can produce a social benefit without having a subsequent harm on those communities Examples of where discriminatory behavior right now is short impact fear of long term impact on civil rights Important to wrestle with fairness Equal credit opportunity tract Talked about bad credit scores and we think they are very effective and have systems that order risk We could break system in U.S. Europe says credit reporting is important because ability to repay is very important so that the consumer is successful data is best when matching data with something that will be successful. Data is commodity you can buy whatever volume you want proxy credit scores not formal credit scores, not using same kind of credit reporting Other factors that will then be in the data that will tend to do the same thing something else will step up this is how large data sets can become problematic Richness of dataset for some communities information could not be treated appropriately in how it s Some people don t have the same methods of engagement don t have the same level of marketing on some folks Solve problem of predictive marketing girth of data to be vulnerable to minority populations Data is driving whole issues and some people get left out while other data can get skewed. The inclusion data divide on how you look at the big pictures Looking at fraud prevention Pick the data source that is broadly used utility data that is used no matter where you live you are using something Telecom also, pushing deeper into communities that are disadvantaged Credit builders Populations of consumers are invisible, those who are underbanked groups experimenting with consumer to build consumer to predict success. Small loans, urban center loans, tribal loans data flows back to traditional reporting o Some people using/reaching new populations that come from other sources.

10 o PEW research data in how we go forward and framing equal credit and housing act drives industries to think about how to reach certain communities Difference between unbanked and underbanked Privacy: mention data obscuring such as re-identification going forward. Privacy and fairness Privacy enhancing technologies if you want to train a classifier, sociocultural data role for using as much information as possible withholding information would be inappropriate in this context Privacy and fairness are important and fundamental in many ways ways are surprising when you think about them at a deeper level structures that govern new things that are happening Big data is immature: there is no firm legislative definition of big data because it doesn t exist, it will, but not yet No global solutions it poses, there are focused and local solutions, what do we do with that not necessary or appropriate How you are choosing the data that is part of statistical parody where are you getting your data? Was it mandatory, was it unknown collection how we deal with fairness and privacy piece Intellectually: Privacy and fairness are not necessarily the same thing people making decisions need ability to discern information presented how similar people are for the same task, best measuring society and math can come up with Define relevant similarity Dealing with abstract & philosophical constructs a lot of discussions in what happens here is going to go back to ethical and discussions to think about issues with data/analytics Framework of transparency and what data looks like people have to understand that data is being used for particular circumstances Define how data is being used internet big buffet on information on how data is used at some point gets muddled Not allow big data to get thrown together big data derived from search engines lenders experimenting and with data sets

11 Structures vs. Unstructured data data more so directly identified if our databases are still based on identifying information in traditional type, then they have to build database among certain lines among accuracy standard As we move forward towards the era of big data should it be based on harm? Should it be based on data collection methods? Use and harm brings us back to that there are very specific ways that people can be damaged sometime more info is good, sometime is bad Issue of medical research perfect example on how to handle big data look at the various ways that the ethics on how privacy works are fascinating if you look at human research subjects research that impacts human subjects Regulatory frameworks that exist sometimes do not apply to narrow applicability need to look at ethical standards Transparency due to experiments show control over personal data lead to risky disclosures information no longer salient Transparency and Control: Providing technology to consumers to control is that a limitation or a solution? Empowering consumers concept to opt out some data socially beneficial purposes in passive data Figure out ways for people to have more knowledge about the internet and the data is collects, when something might do me harm in terms of personal data have option to opt out We need to look at situational nature of data not monolithic framework models to trigger thinking and create sophisticated analysis have produced papers and thoughts Focus on use and harm is alternative way of thinking about things too much weight on thinking about alternatives could be detrimental Responsibility of users is volatile, if we tell them to figure out how data will be used it could lead to more harm than good Don t think structures need to be re-invented keep regulations we have, apply when they apply Make sure fair principles are in place and add statistical parody Where there are not frameworks, we cannot ignore them Problems can exist and problems can be found throw out collection limited Remarks Julie Brill, Commissioner FTC Providing transparency in big data has been done before that categorizes consumers

12 Consumers have been better off with the results, big data analytics bring benefits to consumers and society security preventions to achieve these benefits Consumer trust is critical, transparency, accountability Traditional credit scores and alternative scoring past is prolonged, origins from FCRA teach about current environment First law, businesses able to make quicker decisions about credit worthiness, agencies combined giving consumers access to credit and jobs Accuracy and information increased the growing capabilities of credit reporting bureaus FCRA gives consumers important rights consumers entitled to have access to data, challenge accuracy, notified when they are not accepted for loans FCRA enabled credit reporting enterprise credit scores that first emerged broadened access to credit and made creditworthiness more objective than prior methods Consumers concerned with certain models using variables that act as proxies Congress directed FTC and Federal Reserve to study these questions scores examined did not serve as proxies for race/ethnicity shed lots of light on consumer credit and lessened some concern Other types of scores being used today use of new sources of information is scored consumers has raised questions about how racial/ethnic/ or other lines that the law protects should be addressed If these scores go further they will have to prove that they do not have unwanted impacts on certain populations FTC and other agencies devote serious resources to alternative scoring models, industry shouldn t wait on federal agencies or for congress Data brokers provide massive amounts of data Clients use profiles for marketing and what they should business individual consumers Track sensitive characteristics see clear potential for profiles to harm low income and other vulnerable consumers In ideal world, data brokers could be used to help useful opportunities. However, the same products could be used to make these consumers more vulnerable to high interest payday loans and other economic distress All depends on how products are consumed did not attempt to realize harms that could come from consumer segmentation of poor minority communities Support legislation for greater accountability and transparency for data services

13 Understand profiles being used data broker industry should take stronger proactive steps right now to address potential impact of their products Data brokers should find out how their clients use products, actual uses, inappropriate uses ceased immediately and take steps against future inappropriate use Companies that use their own date to analyze their own data about customers determine what makes them happy They can offer perks to loyal customers, common practices disadvantage certain groups of people exacerbating current socioeconomic disparities Think deeply about where value-added personalization and segmentation ends and harmful discrimination begins All players can take steps not to address potential discriminatory impact of their algorithms hopeful to get them under control and data used to identify disparate treatment and correct such treatment to extent it exists. Presentation: Digging Into the Data, Latanya Sweeney, Chief Technologist FTC & Jinyan Zang, Research Fellow in Tecnhology and Data Governance, FTC Online advertising add to organize to target audiences a lot of ways to make that happen Groups put together add campaigns Data brokers designed to take outside data figuring out when it is to target directly to you to make the connection from end to end targeted advertising something simpler the google network Google 30 billion adds a day every time in every time it takes to load a webpage billions of ads and ad copies and how much they get paid to put that in front of audiences. Google makes decision to which add shows up when Interested in what effects are on the outside Mixed Rank service about capturing online adds get rid of behavioral effects and retargeting effects that cloud doesn t know anything more about you than what you know about yourself OMEGA PSI-PHI supports many black men in colleges what kind of advertising shows up on that site ads that make presumptions about arrest rates and criminal lawyers what kinds of credit cards are they and what they are at? Different websites attract different type of audiences some are skewed towards different audiences and demographics

14 Panel 3: Surveying the Legal Landscape review various antidiscrimination and consumer protection laws and discuss how they may apply to the use of big data, and whether there may be gaps in the law Moderators: Katherine Worthman, Senior Attorney Division of Financial Practices, FTC Patrick Eagan-Van Meter, Program Specialist Division of Financial Practices, FTC Panelists: Leonard Chanin Partner Morrison Foerster Carol Miaskoff Assistant Legal Counsel, Office of Legal Counsel Equal Employment Opportunity Commission Montserrat Miller Partner Arnall Golden Gregory LLP C. Lee Peeler President and CEO of the Advertising Self-Regulatory Council and Executive Vice President, National Advertising Self-Regulation, Council of Better Business Bureaus Peter Swire Professor of Law and Ethics Scheller College of Business, Georgia Institute of Technology Equal Credit Opportunity Act and Regulation B: Law apply to big data marketing and those sorts of thing, illegal to discriminate against an applicant (someone applying to credit), Federal Reserve applied regulation b to certain activities at pre-application stage Law says you cannot discourage a person from applying for credit on prohibited bases Marketing activities you re an existing account holder credit transaction with lender and lender cannot make statements to discourage you from using your credit

15 Federal Reserve in 1985 decided not to expand regulation scope to cover marketing activities Looked in 1998 roughing Regulation B and comment on wherever prescreening should be covered by reviewing Regulation B. Federal Reserve said lenders were using geographical info in terms of marketing and balanced against prescreening that could make more options available, Provide products to people and not allow for disadvantaged products in some targeted neighborhoods Creditors retain information about pre-screening activities creditors have to retain information could be used by enforcement agencies to see if prescreening was being engaged by lenders in an appropriate fashion Equal Credit Opportunity applied broadly credit to corporations, not limited to consumer transactions rules dealing with adverse actions give them credit in most things Title 7 contexts Civil rights and Federal disabilities act very settled law 50 th anniversary of civil rights act this year Basic principles translated in this space how does employment meld with data advertising issues in areas in screening for job and making ultimate selection Gather information about successful employees what prejudices are built into the data and rules built into the data Would it exacerbate past discrimination recruitment is same issue around advertising all jobs occurred there who is getting which jobs, are they accurately distributed/targeted? Law: Interesting in employment space very precise legal terms most suited to big data because that is taking a neutral Look at what is the pool recruitment or selection, What is the tool does it cause and desperate impact? Just because it could cause desperate impact doesn t meant its illegal, only illegal if it doesn t predict accurately success Technology around understanding how to validate FCRA weave into big data and how certain reports in this context consumer friends statue in 70s and regulates credit reporting agencies very specific in how it seeks to regulate and what it seeks to regulate operate in environment for use of data Looking at consumer reports and consumer reporting agencies ecosystem in which these companies operating getting into the data reports couldn t include credit,

16 criminal history, drug history, etc these reports put together by credit reporting agencies for landlords, etc. Looking at use of data for credit, assurance, employment purposes all purposes defined under FCRA looking at due diligence of users, consumer credit agencies operation with accuracy and right to appeal and challenge accuracy and completeness of any report Section 5 Look at application of FTC in big data look at where it s being used and where it s been applied. Distinction btw decision making, granting/denying credit or job, long standing prohibitions using marital status or race in the decision Advertising is going back to products and look at ads for everything they all will be targeted and the best example would be massive targeting FTC programs and educating consumers to step forward and educate consumers themselves Background is advertising deception, whether an act or practice a consumer acting under the circumstances narrowly targeting an audience Challenge to find that harm in marketing is not outweighed to consumers of competition An entrepreneur will be targeting the ads based on section gender Well established public policy statute created that you can use public policy to weigh public policy and benefits but cannot be primary basis for the conclusion that the fact it causes injury Fair Lending Compliance provide fair lending remedies in marketing Violation: Advertisement did not extend offers to customers in Spanish only advertised in English, and left this whole group of people out of a good opportunity If the list is based on aggregate data what are the implications in the FCRA context? Structure to provide CRA with notice and correction if you re sending an ad out prescreening where you have exercised everything to make an offer of credit. Fair lending analysis is very high and unworkable representative of their housing and jobs limited Marketing in lending area: 1. Strongly encouraged targeting to minority communities required remedy in fair lending 2. Split paradox of advertising prohibition on steering when you lend in recent years, targeted subprime loans has raised CFPB concerns Creditor may not advertise practices to encourage borrowers Creditor may not use tactics to

17 discourage borrowers categorical idea pushing CFPB past and previous ways language pro-plaintiff than some language suggests High end credit card and subprime card if the sub card only marketed by minority groups, prime card frequented by high income and non-protected classes, does that count as steering? When is it good to do targeted marketing and when does it do good to steer? Marketing to different audiences/websites doesn t violate equal opportunity act people s response to ads are different from steering people in ads Paper suggests data about demographics for lending online markets that have data inside big data sets Aggregate credit scores: How industry applying FCRA to these scores? Respect employment and FCRA misconception credit scores used for screening request report on individual different not going to improve credit score may include credit information taking that off the table media report that kind of thing Have general aggregate scoring turn more to discussion about impact If someone sends consent to giving credit background using that to an effect or business necessity could be discriminatory bottom line Consent is first step in screening purposes other data not frequently used credit not frequent in position Credit is an area highly reported big data Issue is whether criteria to screen someone for a particular job is relevant for that particular job One of the ways the law is anticipated is that at this point we have guidelines in place about validating selection rules Potential use of Big Data Credit card Company looking at shopping habits of consumers based on where consumers were shopping, lowering limited now you can t purchase data of where people shop Implications with people with better credit terms and employer relying on those who they are advertising jobs Starting with employment is the data causing desperate impact if it were discriminatory, looking to who is preparing for reports Determine whether you are eligible for credit or not Employers looking at social media for screening or not social media learning have to find out what social media is reputational risk

18 Employment at FCRA biggest challenge with social media is accuracy depending on how you capture that social media to see what is available careful not to go beyond the bounds of social media or Instagram to capture information in violation of statutes Panel 4: Considerations on the Path Forward explore best practices for the use of big data to protect consumers Moderator: Christopher Olsen, Assistant Director Division of Privacy and Identity Protection, FTC Panelists: Christopher Calabrese Legislative Counsel American Civil Liberties Union Daniel Castro Senior Analyst Information Technology and Innovation Foundation Jeanette Fitzgerald General Counsel and Chief Privacy Officer Epsilon Jeremy Gillula Staff Technologist Electronic Frontier Foundation Michael Spadea Director Promontory Financial Group Christopher Wolf Senior Partner, Hogan Lovells Founder and Chair, Future of Privacy Forum Chair, National Civil Rights Committee, Anti-Defamation League Discussion today on practices to occur do you agree with the way Big Data is being used? Legal gaps and market barriers that are not being addressed: Data is not bad or good, it just is. It reflects existing disparities in our country across racial lines Data should not exacerbate this disparity but try to make less

19 Major ways Big Data combined today is through a background check Market isn t going to fix certain things about that So many opportunities to fix that unintended consequences of FTC Data isn t natural; it s a thing that is created A lot of self-regulation that already exists Act in an ethical manner known to turn those companies over to the FTC who don t comply Approaching this from technology perspective trying to classify something Classifying this in terms of people puts results on a large scale and hard to tell when you have a false positive or false negative Too early to tell if there is a gap in the regulatory regime back to the harm discussion to do that what the remedy is if you don t know where the harm is? We heard a lot about risk, but not about harm Goal not to develop a perfect regulatory regime, that would kill the economy, how do we allow big data and emerging technologies with the least harm to consumers? Need a threshold of harm, but that shouldn t prevent the good that can come out of the usage Enormous potential for good with Big Data beneficial usages of big data to identify discrimination and big data as a tool to fight discrimination Careful when identifying potential problems and regulating in a way of inhibiting the positive uses of big data Ads being shown where does the self-rank fit into this? Is the anecdote potentially harmful to you? Self-regulation says that if you are part of the industry and groups we are going to use the data in a responsible way and not violate rights but use for marketing purpose only It s a marketing price, if you don t like it, go to another place they are just have offers, not steering Use of aggregated credit score averaging credit scores in a household will determine what kind of paths you will share with people. If we are replicating geographic location in society and different types of ads and offers are problematic, if they are not exposed to a limited amount of offers they think that s the

20 only option and if those offers are dissuading people from receiving credit or ending up with worse credit offers CFPB should push for further regulation Where are the market failures? What s going to happen over time, and if others are getting worse offers then there is possibility for a better company to come in and solve these types of problems? Community banks trying to serve those who the large bank is pulling back big data part of the answer and startups trying to come up with big data points Market responding to the problem Premature to determine whether market failure exists or there are regulatory gaps more work needs to be done to define harm which is a theme we ve heard If more work needs to be done, what is inappropriate or unethical, what are companies doing today in this state of uncertainty? Are they cautions, where are their guidelines in where to act? This is a very different industry those trying to feel their way along with the risk benefit hear more from economists from regulations they have is there an economic benefit trying to spend time with accurate information? Thinking about low income families thinking and hearing more from emphasis and to try and look at review boards how do we look at harm in that type of situation Not advocating for/against harm is critical because companies need clarity on where the risk is and without clarity, it s hard to determine Look at data by case by case basis Predatory lending, using big data inappropriately, using advertising vs financial offerings Consider issues separately and covers benefit analysis for big data and harms and moves privacy impacts forward for big data analysis Are companies engaged in these activities today? Are they undertaking a risk/benefit analysis today? If not, how do we go about doing that? They are doing that today: understand they are under the spotlight by advocates, regulators, media and by consumers New era of transparency they are here and talking about this means companies need to behave responsibly and do the analysis that reflects a moral judgment When talking about private companies, so how do we solve the transparency issue if this sort of analysis isn t public? Big privacy public policy community completely sold out

21 discussion about how to do this better, how do to cost/benefit analysis better asking more from the companies Discussion in earlier panel that there is a lot of public usage in data sets how the data is analyzed and the results published The cost/benefit analysis and benefit/risk analysis sounds like something companies should be doing today. How do we get over transparency hurdle? Inadequate on transparency for now: Data brokers more than 1500 data points on each consumer nowhere near that data points on personal or how that data is being used until I know those assessments individual consumers should know if they are being targeted for certain things Where is the disconnect between reputable companies using data responsibly and the bad actors that have been given the data and not disclosing the usage? More transparency could dull the senses you want the critical information at the moment, in real time dumping more information on consumers might not be protecting, but harming Not giving a notice to consumers in analytical data points not particularly helpful concern on how data being divided and utilized Is there a role for technology in helping to address some of the transparency issues? The technology can help a lot you don t need to show every consumer exactly how they got this ad unless some are interested Through disclosure, not technologically adhesive thing in some way, people could turn big data back on the brokers o Browser plug-in collecting ads you are seeing then you can start to compare the ads people are seeing collect big data on big data sometimes these sort of effects aren t obvious until people can start to compare these things it might not be too transparent Lots of ways you can use this data and if it s something intentional or unintentional could be different What data do you pay for? Getting access to data is easy, but you want to promote that and allow intellectual property and think about regulation and what the data science and what can apply Big Data has tremendous societal value talking about scenarios of regulation vs. no regulation and law vs. no law

22 Should we have a law or legislation on this topic how challenging it is for something to come out of congress are there practices companies can engage in to evaluate potentially harmful risks for big data uses? Having more data would make everyone find more things data about that person used to make more things about that The need for legislation is not there now, it will be down the road when we have further down the road we get and understand the harm in this then we need government intervention What are the specific benefits to data? Lots of benefits in health and such, divides in individuality minorities and women underrepresented in many data some things aren t safe certain disadvantages not received looked at Wikipedia contributions and data deserts in the US are their populations that are overlooked? How do we make sure disadvantaged populations still have data available for them to use? Could see something similar happening are there certain communities excluded? Suggest that at some level, fairness or ethical approach applied as approach or frame to data. 40 different definitions of big data but a fundamental understanding that it relies on volume, variety and velocity that leads to big discoveries Practical issue acknowledged that huge data sets out there that are unstructured To minimize data collection still dealing with huge issue of use and consumers aren t going to make use of transparency options and someone responsible in ecosystem Consumers don t take the time to learn what is being collected about them because they don t have anything to gain from it Responsible use if you own it you are responsible for it Keep it secure and act in responsible manner those would all be tools in the tool box of the organization to act in responsible manner requires strong and resourced regulator because they are going to be the ones to make sense of munch of this. FTC needs a little more muscle to make sure data used responsibly Recommendations: What are the next best steps? Regulators specifically FTC, CFPB investigate equal credit opportunity act does reach some of these practices whether marketing offered to specific populations does discourage people from going after certain offers

23 Predictive analytics part of the conversation thinking about benefits if you care about discrimination or whatever, biggest risk is not how big data being used, we are not using it enough in a regulatory environment Spend time figuring out how to educate consumers on the data used on them and about them teach them that they can talk to companies who have data and ask questions and people can understand who has it and how it is being used data brokers show consumers how data is being used Getting consumers educated about data would be huge benefit be more forthcoming about that sort of thing if you try and find that stuff it s very difficult. Being more transparent and giving general ideas to consumers. FTC look closer and look past height a lot of the benefits from big data are from learning things about a big population it s about individual targeting that is where the majority of the harm is Companies to develop risk programs and data risk framework list of potential risks and how they can apply to your organizations and how to apply those to your specific organization test against them - lf have discussion about harm to FTC: these workshops are great, more workshops Data benefit analysis with respect to big data is something reflexive and something that is talked about and who benefits one size fits all will not have benefits for anything. Closing Remarks Jessica Rich, Director, Bureau of Consumer Protection, FTC Discussion today: Consumer protection issues surrounding big data and its impact in certain consumer groups Workshop parts of FTC look at growing consumer protection issues Many beneficial uses of big data help fight discrimination, predict risk of homelessness, diversity in workplace, healthcare, and empower vulnerable populations Disadvantages of big data individual targets, existing discrimination patterns, and stigmatization could be replicated in the scope of big data predictions developed for one purpose could be used for harmful purposes Need to continue seeking answers How do existing laws apply to big data? How do traditional approaches apply to big data? Transparency and choice apply to this type of environment Steps businesses can take to make sure negative actions will not continue big data only going to get bigger, technology going to make consumers better and adhering to major issues and values

24 FTC identify areas where big data violate the laws enforcement actions where appropriate Examine and raise awareness of consumer protection surrounding big data - avoiding bias and adverse impacts comment period open till Oct 15 file comments electronically or paper