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The Electronic Frontier Foundation reports that DraftKings is using its customers’ own betting records to train a machine learning model that identifies likely losing gamblers, who then receive targeted promotions designed to bring them back to the platform. EFF argues the practice shows why behavioral advertising should be banned outright. DraftKings has not publicly responded to the report’s specific claims.
The Electronic Frontier Foundation (EFF) says DraftKings is using artificial intelligence to identify customers most likely to place losing bets and respond to gambling promotions, then sending them targeted advertising designed to draw them back to the platform. According to the digital rights group, which cited reporting by the New York Times, the company trains a machine learning model on its customers’ own betting records to find these gamblers. EFF is using the practice to renew its call for a ban on all online behavioral advertising.
The EFF’s analysis, published in September 2026, describes the practice as a form of online behavioral advertising — advertising personalized based on data a company has collected about individual users. According to the report, DraftKings uses customer betting histories to train a machine learning model that identifies gamblers who tend to lose money and respond to promotions. Those customers then receive ads and offers intended to re-engage them and encourage further betting that, in EFF’s characterization, the company expects to be losing bets.
EFF argues the practice creates a business incentive problem: gamblers who lose money repeatedly are the customers who generate DraftKings’ revenue, making them the most valuable targets for re-engagement campaigns. The group states that people classified as “problem gamblers” — those who continue gambling despite harm to their finances, relationships, and wellbeing — are highly likely to be targeted by the model. EFF frames this as capitalizing on vulnerability for profit rather than reducing customer risk.
One technical detail in the report stands out: EFF says DraftKings appears to use only “first-party data” — information collected directly from its own users — rather than purchasing additional data from third-party brokers to feed the model. The group presents this as evidence that policy solutions limited to restricting third-party data sharing would not stop the practice, since a company with direct access to rich user data can build such targeting systems on its own.
Why AI-Amplified Ad Targeting Raises Stakes
The report matters because it documents how AI changes the scale and character of behavioral advertising. EFF argues that machine learning magnifies the harms of traditional ad targeting in several ways: it incentivizes the collection of even larger data sets to train and refine models, and it processes that data far faster than previous methods. Because AI systems operate as a “black box,” EFF writes, the engineers building them often cannot predict which data points the model will find useful, which drives continuous expansion of data collection.
The group also connects ad-targeting data to the broader surveillance industry, noting that data collected for ad placement is sold to insurance companies, banks, and government law enforcement agencies including CBP. It cites a Request for Information published by ICE earlier in 2026 seeking information on how commercial big data and ad tech providers could support investigations.
For gamblers and regulators, the specific concern is the vulnerability of the targeted population. If a model reliably identifies people prone to losses, the same capability could in principle be used to flag and assist problem gamblers — a tension at the center of EFF’s argument that the incentive structure points the other way.
EFF’s Longstanding Ban Campaign
EFF states it has long argued that all behavioral advertising should be banned, positioning the DraftKings example as the latest illustration of its position rather than a new campaign. The group’s argument is structural: if companies could not send personalized ads, they would have less incentive to collect the behavioral data that powers them.
The DraftKings report fits into a wider debate over sports betting expansion in the United States, where legal online gambling has grown rapidly since a 2018 Supreme Court decision allowed states to legalize it. Regulators in several states have pressed operators on responsible-gambling tools, deposit limits, and advertising practices. EFF’s report adds a data-and-AI dimension to that debate, arguing that self-collected first-party data can be used for targeting without any third-party data sharing taking place.
“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.”
— Electronic Frontier Foundation
What DraftKings Hasn’t Addressed
EFF’s analysis is an advocacy organization’s characterization of the practice, not a regulatory finding or a disclosure from DraftKings itself. The core factual claims — that a model is trained on betting records to find losing gamblers, and that those users receive targeted promotions — rest on New York Times reporting as cited by EFF. DraftKings’ own description of the program, its internal safeguards for problem gamblers, and any response to the report have not been included in the available material.
It is also not clear how many customers have been targeted, what percentage of targeted users are problem gamblers, whether any regulators have opened inquiries, or what specific data points the model uses. The report does not allege that DraftKings buys third-party data, but EFF’s evidence for the first-party-only claim is not detailed in the available material.
Regulatory and Policy Pressure Ahead
EFF is calling on policymakers to ban online behavioral advertising outright, arguing that narrower rules on third-party data sales would leave first-party targeting intact. Whether any legislative body takes up that approach remains to be seen; existing US privacy proposals have generally focused on data broker regulation and opt-out rights rather than full bans on personalized ads.
State gambling regulators, which oversee sports betting operators directly, could also examine whether targeting losing gamblers conflicts with responsible-gambling requirements. A response from DraftKings, further reporting on the model’s scale, and any regulatory reaction would clarify the practical fallout. EFF also points readers to its Surveillance Self-Defense project for steps individuals can take to limit data collection on mobile apps and websites.
Key Questions
What exactly is DraftKings accused of doing?
According to EFF, citing the New York Times, DraftKings trains a machine learning model on customers’ betting records to identify gamblers likely to lose money and respond to promotions, then sends them targeted advertising intended to bring them back to the platform.
Is this confirmed by DraftKings?
No public response from DraftKings is included in the available material. The claims come from EFF’s advocacy analysis, which attributes the underlying reporting to the New York Times, and remain unverified by the company or regulators.
Does DraftKings buy data from third parties for this?
According to EFF, the company appears to use only first-party data — information collected directly from its own users. EFF uses this point to argue that limiting third-party data sales would not stop the practice.
Why does EFF want behavioral advertising banned entirely?
EFF argues that personalized ads create the incentive to collect vast amounts of behavioral data, that AI amplifies both collection and processing speed, and that ad-targeting data feeds the wider surveillance industry, including sales to insurers, banks, and government agencies.
What can individual users do?
EFF points to its Surveillance Self-Defense project and other guidance for limiting data collection on mobile apps and websites, while emphasizing that its preferred solution is a policy ban on behavioral advertising rather than individual countermeasures.
Source: hn
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