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.
Facts Only
* DraftKings uses AI to target customers likely to place losing bets.
* The company trains machine learning models using customers' betting records.
* Targeted advertising is sent to identified losing gamblers to lure them back to the site.
* DraftKings utilizes first-party data for these promotions.
* Behavioral advertising involves personalizing ads based on collected user data.
* ICE published a Request for Information regarding commercial Big Data and Ad Tech providers.
* Data from ad tech is sold to banks, insurance companies, and law enforcement agencies.
* The EFF advocates for a total ban on online behavioral advertising.
* The EFF provides the Surveillance Self Defense project for data protection.
* CBP is among the government agencies receiving data from the surveillance industry.
Executive Summary
DraftKings utilizes machine learning models trained on customer betting records to identify users with a high probability of losing bets. Once identified, these individuals receive targeted promotions designed to encourage further wagering. This strategy specifically risks targeting "problem gamblers"—those who continue to gamble despite significant personal or financial harm—as these users represent a primary source of revenue for the company.
The use of AI accelerates the scale and speed of this behavioral advertising, necessitating the collection of larger datasets. While DraftKings relies on first-party data, the broader infrastructure of behavioral advertising often feeds a surveillance ecosystem where data is shared with insurance companies, banks, and government agencies such as CBP and ICE. Because first-party data can be used for these predatory outcomes, advocates argue that limiting third-party data sharing is insufficient and that a total ban on online behavioral advertising is the only effective policy solution.
Full Take
The strongest version of this narrative is that AI has transformed behavioral advertising from a marketing tool into a precision instrument for exploitation, specifically by identifying and monetizing human vulnerability in real-time. By automating the search for "losing gamblers," the system removes the friction that might otherwise protect a problem gambler from their own impulses, creating a feedback loop where the most harmed users are the most targeted.
The narrative employs a False Binary by presenting the solution as a choice between ineffective third-party data limits and a total ban on behavioral advertising, omitting potential middle-ground regulatory frameworks such as mandatory "cooling-off" periods for identified high-risk users or strict transparency requirements for AI-driven targeting.
Patterns detected: ARC-0047 False Binary
This situation is driven by the "Efficiency Paradigm," where the ability to optimize for a metric (revenue) overrides the ethical obligation to avoid harm. It echoes the historical pattern of "predatory inclusion," where marginalized or vulnerable populations are given access to a service specifically because they can be extracted from at a higher rate.
The implication is a steady erosion of cognitive sovereignty; when an AI knows a user's breaking point better than the user does, "choice" becomes an illusion managed by the platform. The benefit accrues to the platform's bottom line, while the cost is borne by the individual's financial stability and mental health.
Bridge Questions:
* If AI can identify "problem gamblers" for profit, could the same models be legally mandated to identify them for mandatory intervention?
* Does the ban on behavioral advertising solve the underlying issue of data collection, or simply change the incentive for how that data is used?
Counterstrike Scan:
A coordinated influence campaign would likely use "Moral Panic" by hyper-focusing on the most extreme cases of gambling addiction to demand immediate, sweeping legislation that eliminates competition for larger data aggregators. The current content remains a focused critique of a specific corporate practice and a general policy stance, which is a healthy expression of advocacy rather than a structural attack pattern.
Sentinel — Human
The text reads like a synthesized journalistic piece that effectively uses a specific case study to argue for broader regulatory change regarding online behavioral advertising, exhibiting strong human analytical structure.