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Most Americans Don’t Realise What Companies Can Predict From Their Data

Data can be used to predict other kinds of information– information people might not be comfortable with revealing.

Sixty-seven percent of smartphone users rely on Google Maps to help them get to where they are going quickly and efficiently.

A major of feature of Google Maps is its ability to predict how long different navigation routes will take. That’s possible because the mobile phone of each person using Google Maps sends data about its location and speed back to Google’s servers, where it is analyzed to generate new data about traffic conditions.

Information like this is useful for navigation. But the exact same data that is used to predict traffic patterns can also be used to predict other kinds of information – information people might not be comfortable with revealing.

For example, data about a mobile phone’s past location and movement patterns can be used to predict where a person lives, who their employer is, where they attend religious services and the age range of their children based on where they drop them off for school.

These predictions label who you are as a person and guess what you’re likely to do in the future. Research shows that people are largely unaware that these predictions are possible, and, if they do become aware of it, don’t like it. In my view, as someone who studies how predictive algorithms affect people’s privacy, that is a major problem for digital privacy in the U.S.

How is this all possible?

Every device that you use, every company you do business with, every online account you create or loyalty program you join, and even the government itself collects data about you.

The kinds of data they collect include things like your name, address, age, Social Security or driver’s license number, purchase transaction history, web browsing activity, voter registration information, whether you have children living with you or speak a foreign language, the photos you have posted to social media, the listing price of your home, whether you’ve recently had a life event like getting married, your credit score, what kind of car you drive, how much you spend on groceries, how much credit card debt you have and the location history from your mobile phone.

What data brokers know

Category Data
Identifying data Name, previously used names, address, address history, longitude and latitude, phone numbers, email address
Sensitive identifying data Social Security number, driver’s license number, birthdate, a birthdate of family members in the household
Demographic data Age, height, weight, gender, race/ethnicity, country of origin, religion (by surname at household level), language, marital status, presence of elderly parent, presence of children in household, education level, occupation, family ties, demographic characteristics of family members in household, number of surnames in household, veteran in household, grandparent in house, Spanish speaker, foreign language household, households with a householder who is Hispanic origin or Latino, employed (white or blue collar occupation), work at home, length of residence, household size, congressional district, single parent with children, ethnic and religious affiliations
Court and public record data Bankruptcies, criminal offences and convictions, judgments, liens, marriage licenses, state licenses and registrations, voting registration and party identification
General interest data Apparel preferences, attendance at sporting events, charitable giving, gambling (casinos or state lotteries), thrifty elders, life events (e.g., retirement, newlywed, expectant parent), magazine and catalog subscriptions, media channels used, participation in outdoor activities (e.g., golf, motorcycling, skiing, camping), participation in sweepstakes or contests, pets, dog owner, political leanings, assimilation code, preferred celebrities, preferred movie genres, preferred music genres, reading and listening preferences, donor (e.g., religious, political, health causes), financial newsletter subscriber, upscale retail card holder, affluent baby boomer, working-class moms, working woman, African-American professional, biker/Hell’s Angels, Bible lifestyle, New Age/organic lifestyle
Social media data Electronics purchases, friend connections, internet connection type, internet provider, level of usage, heavy Facebook user, heavy Twitter user, Twitter user with 250+ friends, is a member of more than 5 social networks, online influence, operating system, software purchases, type of media posted, uploaded pictures, use of long-distance calling services, presence of computer owner, use of mobile devices, social media and internet accounts (e.g., on LinkedIn, Amazon, Flickr, NBA.com)
Home and neighbourhood data Census tract data, address coded as public/government housing, dwelling type, heating and cooling, home equity, home loan amount and interest rate, home size, lender type, length of residence, listing price, market value, move date, neighbourhood criminal/demographic/business data, number of baths, number of rooms, number of units, presence of fireplace, presence of garage, presence of home pool, rent price, type of owner, type of roof, year built

 

It doesn’t matter if these datasets were collected separately by different sources and don’t contain your name. It’s still easy to match them up according to other information about you that they contain.

For example, there are identifiers in public records databases, like your name and home address, that can be matched up with GPS location data from an app on your mobile phone. This allows a third party to link your home address with the location where you spend most of your evening and nighttime hours – presumably where you live. This means the app developer and its partners have access to your name, even if you didn’t directly give it to them.

In the U.S., the companies and platforms you interact with own the data they collect about you. This means they can legally sell this information to data brokers.

Data brokers are companies that are in the business of buying and selling datasets from a wide range of sources, including location data from many mobile phone carriers. Data brokers combine data to create detailed profiles of individual people, which they sell to other companies.

Combined datasets like this can be used to predict what you’ll want to buy in order to target ads. For example, a company that has purchased data about you can do things like connect your social media accounts and web browsing history with the route you take when you’re running errands and your purchase history at your local grocery store.

Employers use large datasets and predictive algorithms to make decisions about who to interview for jobs and predict who might quit. Police departments make lists of people who may be more likely to commit violent crimes. FICO, the same company that calculates credit scores, also calculates a “medication adherence score” that predicts who will stop taking their prescription medications.

Research shows that people are only aware of predictions that are shown to them in an app’s user interface, and that make sense given the reason they decided to use the app. SIFO CRACHO/shutterstock.com

How aware are people about this?

Even though people may be aware that their mobile phones have GPS and that their name and address are in a public records database somewhere, it’s far less likely that they realize how their data can be combined to make new predictions. That’s because privacy policies typically only include vague language about how data that’s collected will be used.

In a January survey, the Pew Internet and American Life project asked adult Facebook users in the U.S. about the predictions that Facebook makes about their personal traits, based on data collected by the platform and its partners. For example, Facebook assigns a “multicultural affinity” category to some users, guessing how similar they are to people from a different race or ethnic backgrounds. This information is used to target ads.

The survey found that 74 per cent of people did not know about these predictions. About half said they are not comfortable with Facebook predicting information like this.

In my research, I’ve found that people are only aware of predictions that are shown to them in an app’s user interface, and that makes sense given the reason they decided to use the app. For example, a 2017 study of fitness tracker users showed that people are aware that their tracker device collects their GPS location when they are exercising. But this doesn’t translate into awareness that the activity tracker company can predict where they live.

In another study, I found that Google Search users know that Google collects data about their search history, and Facebook users are aware that Facebook knows who their friends are. But people don’t know that their Facebook “likes” can be used to accurately predict their political party affiliation or sexual orientation.

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