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.
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.