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The Electronic Frontier Foundation reports that DraftKings uses a machine learning model trained on customers’ betting records to identify losing gamblers and target them with promotions designed to bring them back to the platform. The EFF says problem gamblers are highly likely to be caught by this targeting and is calling for a ban on behavioral advertising.
The Electronic Frontier Foundation (EFF) says DraftKings is using a machine learning model trained on its customers’ own betting records to identify gamblers likely to place losing bets, then sending them targeted advertising and promotions designed to bring them back to the platform. The digital rights group, citing reporting by the New York Times, argues the practice disproportionately harms problem gamblers and illustrates how AI amplifies the damage of online behavioral advertising.
According to the EFF’s September 2026 analysis, DraftKings’ model works by analyzing users’ historical betting activity to find customers whose wagers are expected to lose. Once identified, those customers receive promotions aimed at re-engaging them on the site to place more bets — bets that, according to the report, DraftKings’ own model predicts will be losing ones. The EFF notes that losing gamblers are the customers who actually generate the company’s profits, creating what the group describes as a business incentive to keep them betting.
The EFF states that people considered “problem gamblers” — those who continue gambling despite harm to their finances, relationships, and wellbeing — are highly likely to be captured by this targeting model. The group frames the practice as capitalizing on vulnerability for profit rather than mitigating risk.
The report also notes a technical detail with policy implications: DraftKings appears to use only “first-party data” — information collected directly from its own users — rather than purchasing third-party data. The EFF argues this means policy solutions focused solely on limiting third-party data sharing would fail to prevent this kind of targeting.
Why AI-Driven Gambling Ads Raise Stakes
The EFF’s central argument is that AI magnifies harms that already existed in behavioral advertising. Machine learning systems operate as black boxes, the group says: engineers often cannot predict which data points a model will find useful, which drives continuous collection of ever-larger quantities of data. AI also allows companies to process enormous datasets far faster than before.
For gamblers specifically, the practice means that the people most likely to be targeted with re-engagement offers are those least able to absorb losses. Sports betting is legal and rapidly expanding across the United States, and the report suggests other operators could adopt similar techniques, since the underlying approach — training models on first-party behavioral data — requires no external data purchases.
The EFF also warns of a broader downstream consequence: data collected for targeted advertising feeds the wider surveillance industry, with ad-derived data being sold to insurers, banks, and government agencies including CBP. The group points to a Request for Information published by ICE earlier in 2026 seeking information on how commercial big data and ad tech providers could support investigations.
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EFF’s Longstanding Ad Ban Position
The EFF’s report builds on its long-held position that all behavioral advertising should be banned, not merely regulated. The group’s reasoning: if companies cannot send personalized ads, they lose the incentive to collect the behavioral data that powers them.
Online behavioral advertising — personalizing ads based on collected user data — predates AI. The EFF argues that AI-driven targeting is an escalation rather than a new category of harm, but one that makes data collection more expansive and processing faster. The group also points readers to its Surveillance Self-Defense project and other resources for limiting data exposure on mobile apps and websites.
“DraftKings is using AI to supercharge the harmful effects of online behavioral advertising.”
— Electronic Frontier Foundation
What the DraftKings Report Leaves Open
DraftKings has not publicly confirmed the details of the targeting model described in the report, and the EFF’s account relies on New York Times reporting rather than on published company documentation. The exact mechanics of the model — what data points it uses, how it defines a “losing gambler,” and what share of customers it flags — are not disclosed.
It is also unclear how DraftKings’ model interacts, if at all, with responsible-gambling tools the company offers, or whether any internal limits exist on targeting customers who show signs of gambling problems. The EFF’s characterization of the practice as “predatory” is the group’s own assessment and framing, not a legal finding.
Regulatory Pressure and Policy Responses
The EFF is calling on policymakers to ban online behavioral advertising outright, arguing that restrictions limited to third-party data sales would not stop practices like DraftKings’, which rely on first-party data. The report’s publication is likely to add to existing regulatory scrutiny of sports-betting operators’ use of customer data in the United States and elsewhere.
Any formal response from DraftKings, state gambling regulators, or lawmakers had not been made public at the time of the report. Consumer advocates have previously pushed for stricter rules on gambling promotions, and the intersection of AI targeting and gambling addiction may become a focus of future legislative proposals.
Key Questions
What exactly is DraftKings accused of doing?
According to the EFF, citing New York Times reporting, DraftKings trains a machine learning model on customers’ betting records to identify gamblers likely to place losing bets, then sends them targeted promotions designed to bring them back to the platform.
Is DraftKings using third-party data for this targeting?
No, according to the EFF. The group says DraftKings appears to rely solely on first-party data — information collected directly from its own users — which is why it argues that limiting third-party data sales alone would not prevent this practice.
Has DraftKings confirmed the report?
No public confirmation from DraftKings was included in the EFF’s report. The account is based on New York Times reporting and the EFF’s analysis, and details of the model’s mechanics remain undisclosed.
Why does the EFF want behavioral advertising banned entirely?
The EFF argues that if companies cannot send personalized ads, they lose the incentive to collect the behavioral data powering them. It also says AI amplifies harms by driving more data collection and enabling faster processing of large datasets.
What can individuals do to limit this kind of targeting?
The EFF points to its Surveillance Self-Defense project and other guidance for protecting personal data on mobile apps and websites, though it emphasizes that individual protections are not a substitute for policy change.
Source: hn
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