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The Electronic Frontier Foundation says DraftKings uses a machine learning model trained on customers’ betting records to identify people likely to place losing bets and target them with promotions. The account cites The New York Times; DraftKings’ response and details about the model’s operation are not included in the source material.
The Electronic Frontier Foundation says DraftKings uses a machine learning model trained on customers’ betting records to identify people likely to place losing bets, then sends them targeted promotions to encourage more betting. Citing The New York Times, the EFF describes a practice that raises concerns about using personal gambling data to reach customers who may be vulnerable to gambling-related harm.
According to the EFF’s account of The New York Times report, DraftKings analyzes customers’ betting records to find gamblers the company’s model predicts will lose. It then targets those customers with advertising intended to bring them back to the platform. The source does not provide the model’s accuracy, the number of customers affected or examples of specific promotions.
The EFF says DraftKings appears to rely on first-party data—information it collects directly from its own users—rather than additional data bought from outside brokers. That detail matters to debates over privacy rules: restricting the sale or sharing of third-party data alone might not prevent a company from using its own customer records to personalize advertising.
The EFF characterizes people who repeatedly gamble despite harm to their finances, relationships or well-being as problem gamblers, and argues they may be especially likely to be targeted by a model designed to find losing customers. That is the advocacy group’s assessment; the source does not establish how DraftKings defines risk or whether the model identifies people with a gambling disorder.
Promotions Reach Losing Customers
The reported practice links prediction of betting losses to marketing intended to prompt further gambling. If the account is accurate, promotions could reach customers because their past behavior suggests they are profitable to the sportsbook, even when continued betting may harm those customers. The EFF argues that this creates an incentive to re-engage people at risk instead of reducing that risk.
The report also highlights a limit of privacy proposals focused only on data brokers. A company can personalize ads with information it collects itself, so rules addressing only outside data sales may leave this kind of targeting untouched. The EFF’s policy position is that behavioral advertising should be banned; that is its recommendation, not an existing rule described in the source.
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How Betting Data Fuels Targeting
Behavioral advertising uses information about people to tailor the ads they see. In this case, the EFF says DraftKings applies machine learning to its customers’ betting histories to select whom to target with promotions. The source frames AI as accelerating the processing of large data sets and increasing incentives to collect information, while warning that model decisions can be difficult for people outside the process to understand.
The EFF places the case within broader concerns about ad-tech data. It says information gathered for personalized advertising can circulate to organizations including insurers, banks and government agencies. The group also points to an Immigration and Customs Enforcement request for information earlier in 2026 about commercial big-data and ad-tech providers. These are wider concerns raised by the EFF; the source does not say that DraftKings provided its customer records to those entities.
“DraftKings is using AI to target customers who are most likely to place losing bets and respond to gambling promotions.”
— Electronic Frontier Foundation
Model Reach Remains Unknown
The source material does not include a statement from DraftKings, so the company’s response is not available here. It also does not specify how many users are targeted, how the model defines likely losses, what data points it uses beyond betting records, or how often its predictions are accurate. The material does not establish whether customers can opt out of these promotions or whether the company uses safeguards to identify and limit harm.
The EFF says the company appears to use first-party data, but does not provide a detailed account of how that conclusion was reached. The distinction between customers predicted to lose bets and people experiencing gambling problems also remains important: the source does not show that the model diagnoses or directly measures a gambling disorder.
Company Response and Safeguards
The source provides no announced next milestone, regulatory action or company response. Further information from DraftKings or additional reporting could clarify the model’s scope, the promotions sent, and what options customers have to limit targeted marketing. For now, the reported use of betting records and the EFF’s concerns frame a wider policy debate over whether rules should cover a company’s use of its own customer data, as well as data sold by third parties.
Key Questions
What does the EFF say DraftKings is doing?
The EFF, citing The New York Times, says DraftKings trains a machine learning model on customers’ betting records to find people likely to place losing bets and sends them promotions intended to bring them back to the platform.
Does the report say DraftKings buys outside data for this model?
The EFF says DraftKings seems to use first-party data collected directly from its customers. The source does not describe a detailed data audit or say that third-party data is part of the model.
Does the source establish that the model targets people with a gambling disorder?
No. The EFF argues that people who repeatedly gamble despite harm may be likely to fall within the group targeted, but the source does not show that the model diagnoses gambling disorder or explain how it measures customer vulnerability.
What details about the targeting are still unknown?
The source does not give the number of customers affected, the model’s accuracy, the specific data inputs, DraftKings’ response or details of safeguards and opt-out options.
Source: hn
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