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The Electronic Frontier Foundation says DraftKings uses a machine learning model trained on customers’ betting records to identify losing gamblers and send them promotions to return to betting. The account, which EFF attributes in part to The New York Times, raises questions about targeting people at risk of gambling harm; details about the model and safeguards are not provided in the supplied report.
The Electronic Frontier Foundation says DraftKings uses a machine learning model trained on customers’ betting records to identify people likely to make losing bets and send them promotions intended to bring them back to the sportsbook. The report, citing The New York Times for its account of the model, raises concerns that promotions may reach people experiencing gambling problems, though the supplied material does not detail the model’s operation or DraftKings’ safeguards.
According to the EFF, DraftKings analyzes users’ betting histories to find customers it expects to lose money and respond to gambling promotions. The company then sends those customers targeted advertisements designed to encourage further betting, the advocacy group says. The report characterizes the approach as behavioral advertising: promotions personalized using information collected about a person.
EFF says DraftKings appears to rely on first-party data—information it gathers directly from its own customers—rather than buying additional data from outside brokers to train the model. That distinction matters to policy proposals focused on restricting the sale or sharing of third-party data: such limits would not necessarily prevent a company from using its own customer records to personalize promotions.
The material provided does not include a response from DraftKings, the model’s technical documentation, or figures showing how many users receive these promotions. It also does not establish how the company defines a likely losing bettor, whether the model identifies people with gambling disorders, or what limits apply to outreach. EFF’s account connects the practice to potential harm, but those operational details remain unconfirmed here.
Promotions May Reach At-Risk Bettors
The reported practice puts the commercial use of betting data in direct tension with efforts to reduce gambling harm. If a platform identifies customers expected to lose money and uses that prediction to encourage another visit, the same records that help personalize marketing may also flag people who could be vulnerable to further losses. EFF argues that people it describes as problem gamblers—those who continue gambling despite harm to their finances, relationships or well-being—could be among those targeted.
The report does not establish that every customer identified by the model has a gambling problem, or quantify resulting losses. Still, the possibility that a prediction of likely losses could inform promotional outreach raises questions for customers, regulators and lawmakers about what companies may do with behavioral data and how they should handle signs of risky gambling.
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First-Party Data Limits Policy Fixes
Behavioral advertising uses information about people to tailor the ads or promotions they see. EFF has argued for a ban on the practice, saying it creates incentives to collect extensive personal data. In its September 2026 report, the group says AI can make that system more powerful by processing large datasets quickly and using them to refine predictions.
EFF also links the broader advertising technology industry to data flows beyond marketing, saying collected data can be sold to insurers, banks and government agencies. The supplied report mentions an earlier Immigration and Customs Enforcement request for information about commercial big data and ad tech providers. That wider context is EFF’s argument about the risks of data collection; it does not show that DraftKings shared this betting data with those entities.
“DraftKings is using its customers’ betting records to train a machine learning model to find losing gamblers.”
— Electronic Frontier Foundation
Model Rules and Safeguards Unknown
The supplied report does not include a statement from DraftKings, nor does it specify the model’s inputs beyond betting records, its accuracy, or the criteria used to select customers for promotions. It is also unclear whether the company uses any indicators of gambling-related harm, how customers can opt out of this targeting, and what protections may prevent promotions from reaching people seeking help or limiting their play. The reported approach does not by itself confirm that every person targeted is a chronic or problem gambler.
Company and Regulators May Respond
Further information from DraftKings could clarify how the model works, what data it uses, and whether the company excludes customers who have self-excluded or shown signs of harm. The supplied material does not identify a regulatory action or an announced company change. The next developments to watch are any public response from DraftKings and whether lawmakers or gambling regulators address AI-driven promotional targeting using a company’s own customer data.
Key Questions
What does EFF say DraftKings is doing?
EFF says DraftKings uses customers’ betting records to train a machine learning model that identifies people it predicts will make losing bets, then sends them promotions intended to bring them back to the platform.
Does the report prove that everyone targeted has a gambling problem?
No. The report says people experiencing gambling problems may be especially likely to be targeted, but it does not say that every customer selected by the model has a gambling disorder or provide details of the selection criteria.
What data does DraftKings reportedly use?
EFF says the company appears to use first-party data collected directly from its customers. The supplied material does not provide a full list of the data used by the model.
Has DraftKings commented or announced changes?
No company response or change is included in the source material provided. Whether DraftKings will respond or alter its targeting practices remains unclear.
Source: hn
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