In an extremely disappointing ruling, a California trial court dismissed SIREN v. San Jose, our case challenging the city of San Jose’s warrantless searches of its vast stores of automated license plate reader (ALPR) data. EFF and the ACLU of Northern California represent SIREN and CAIR California, two non-profits that serve San Jose residents, in this case.

Make no mistake: We believe this ruling is wrong on the law, and we will appeal. The ruling ignores the importance of landmark rulings from the U.S. Supreme Court regarding the invasiveness of aggregating collected data on an individual over time, including one issued just months ago. It also severely underestimates the harm that ALPRs cause, in San Jose and around the country. 

San Jose’s ALPR surveillance system is inescapable. It blankets the city’s streets in nearly five hundred cameras that continuously photograph every car that passes by and retains the collected data for a month or longer. In our briefs, we argued that law enforcement officers who warrantlessly search the resulting ALPR database are violating the California Constitution’s ban on unreasonable searches and its guarantee of privacy. The court disagreed, explaining that based on one California appeals court case involving individual streetlight cameras, people do not have a reasonable expectation of privacy when driving on a public road. This is wrong. Networked ALPRs are not individual streetlight cameras. Their net effect of tracking and mapping cars as they travel reveals patterns of life, intimate relationships, and much more.

Fortunately, there are signs that the tide is turning against ALPRs in the courts. Just a day before the ruling in our case, a federal district court in Oklahoma called ALPRs “a type of indiscriminate mass surveillance” and decided that a law enforcement officer’s warrantless search of ALPR data violated the Fourth Amendment. The officer was observing traffic on an Oklahoma highway when he saw a woman driving a car with California plates. According to the court order, the officer began following the woman “for no apparent reason” other than her out of state plates. While following her, the officer decided to run the woman’s license plate number through Flock’s ALPR system – again, with no apparent reason other than her California plates. The Flock search returned at least 30 days of data, allowing the officer to track the car across at least three states, over the span of a month, from a single search.

The facts of this case demonstrate how ALPRs allow law enforcement to grab a breathtakingly vast sweep of information about a car (and the driver within) with no more effort than a Google search. It’s true that when a person drives on public roads, they can be observed, including by police. But as the Supreme Court has explained, we don’t expect law enforcement to be able to secretly track and store every movement our cars while we’re driving. The federal court in the Oklahoma case aptly illustrated this difference in societal expectations:

We might reasonably expect our friends and family to tell us something like: “Hey, I saw you out driving by the store last week.” But we do not expect people we know—let alone strangers, and especially not law enforcement—to randomly approach us to say: “Hey, I have been following your car for the last 30 days, and here is a detailed log of every place your vehicle has been with supporting photographic evidence. I am also going to share this information with law enforcement throughout the country so they know your whereabouts, too.

This decision is the first of its kind, and EFF will continue working to ensure that it is the first of many. Outside the courts, we’ve seen people across the country step up to reject ALPRs at the state and local level. EFF will continue challenging this technology on every front as well.

Online sports betting company DraftKings is using AI to target customers who are most likely to place losing bets and respond to gambling promotions. This kind of targeting is a form of online behavioral advertising, which is when companies personalize the ads they show you based on the data they’ve collected about you. The more data a company has, the more personalized the ad can be. While DraftKings is using AI to supercharge the harmful effects of online behavioral advertising, EFF has long argued that all behavioral advertising should be banned. 

According to the New York Times, DraftKings is using its customers’ betting records to train a machine learning model to find losing gamblers. Once found, DraftKings sends these customers targeted advertising designed to lure them back to the site to place more bets—bets that DraftKings thinks will be losing ones. DraftKings has a business incentive to keep losing gamblers coming back to their site, because these are the users actually making DraftKings money. Unfortunately, those considered “problem gamblers” (people who repeatedly gamble despite harm to themselves, their finances, and their relationships) are highly likely to be targeted by this model. By re-engaging these individuals through targeted promotions aimed at keeping them on the platform, DraftKings is capitalizing on their vulnerability for profit instead of mitigating their risk. 

Predatory online behavioral advertising isn’t new, but companies’ use of AI to process data and target customers has magnified its harms. Online behavioral advertising incentivizes the collection of vast quantities of data to power ad tech. Adding AI into the mix means that even more data is collected to train and refine models. Because AI operates as a black box, the humans building the models can rarely predict which data points are the most useful to the AI, driving them to continuously collect more data. AI also allows companies to process enormous data sets much faster, and, as a result, supercharges the harms of online behavioral advertising. 

A direct consequence of online behavioral advertising is that it provides the data the surveillance industry needs to run. Data collected for targeted placement of ads is being sold to insurance companies, banks, and state and federal government law enforcement agencies such as CBP. ICE is also taking an interest in the data fueling ad tech: earlier this year, ICE published a Request for Information “seeking information to better understand how the industry’s commercial Big Data and Ad Tech providers can directly support investigations activities.”  

DraftKings seems to be using solely “first party data” to target their ads, meaning that they’re using only the data they collect directly from their users and are not buying any additional data from third parties to fuel their machine learning model. This highlights how policy solutions that only limit third-party data sharing and selling would not be enough to prevent these predatory advertisements. Rather, policymakers must ban online behavioral ads.   

What DraftKings is doing with their targeted promotions is just one example of how online behavioral advertising causes real harm to real people. But there are ways to take back control over your own data: EFF offers resources such as our Surveillance Self Defense project, along with other tips for how you can protect yourself on mobile apps and on websites.  

DraftKings’ use of AI to target losing gamblers illustrates how ad tech evolves and how companies find new ways to use our data against us. This is why EFF believes that all behavioral advertising should be banned. If companies can’t send personalized ads, they’ll have less incentive to collect the behavioral data powering them.