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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 make losing bets and target them with promotions. The account is based on reporting by The New York Times; DraftKings’ response, the model’s criteria and its measured effects are not provided in the material.
The Electronic Frontier Foundation says DraftKings uses a machine-learning model trained on customers’ betting records to identify gamblers likely to place losing bets, then sends those customers promotions intended to bring them back to its sportsbook. The account cites reporting by The New York Times; the source material does not include a response from DraftKings or independent details about how the system operates.
According to the EFF’s account of the Times reporting, DraftKings uses its own customers’ betting records to train a model that identifies people it expects to lose. The company then sends selected customers targeted gambling promotions. The EFF says the promotions are designed to encourage more betting, including bets the model predicts will be losing ones for the customers.
The EFF describes the approach as online behavioral advertising: personalizing advertising based on information collected about a person. It says DraftKings appears to rely on first-party data—information gathered directly from its own users—rather than data purchased from outside brokers. The source material does not specify which records or other user data feed the model, how the predictions are evaluated, or how many customers receive these promotions.
The EFF argues that people it describes as problem gamblers, who continue gambling despite harm to their lives, finances or relationships, may be especially vulnerable to this targeting. It characterizes the practice as exploiting that vulnerability for profit. That is the advocacy group’s assessment; the provided account does not quantify the number of affected customers or establish how the promotions change their betting behavior.
Targeted Promotions Reach Losing Bettors
The reported practice raises questions about how a betting company uses customer information when its commercial interest may conflict with customers’ financial well-being. If a model identifies people expected to lose and the company targets them with offers to return, promotions could reach customers at elevated risk of gambling-related harm. The source material does not establish the model’s accuracy or prove that the promotions caused additional losses.
The use of first-party data also matters for policy debates. The EFF says DraftKings appears to use information gathered directly from customers. On that account, rules focused only on the sale or sharing of data by third parties may not address targeting based on a company’s own records. The EFF uses the case to support its position that behavioral advertising should be banned.
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How Betting Records Feed Ad Targeting
Behavioral advertising uses information about people to select or personalize the ads they see. In this case, the EFF says DraftKings’ betting records are used to train a machine-learning model, which then helps select customers for gambling promotions. The source does not describe when the system was introduced or whether it operates across all DraftKings products.
The EFF argues that using AI can intensify existing concerns around behavioral advertising because models can process large data sets quickly and may be difficult for their developers to interpret. It also warns that data collected for advertising can move beyond its original use. As examples of wider ad-tech data concerns, the EFF points to commercial data being sold to institutions including insurers, banks and government agencies, and cites an ICE request for information about commercial big-data and ad-tech providers. Those examples concern the broader industry; the provided material does not say DraftKings supplied data to those entities.
“DraftKings is using its customers’ betting records to train a machine learning model to find losing gamblers.”
— Electronic Frontier Foundation
Model Scope and Effects Remain Unknown
The material does not include DraftKings’ response, documentation from the company, or the underlying Times report. It therefore leaves open how the model works, which customer records it uses, what counts as a likely losing bettor, and whether customers can opt out of this particular targeting.
It is also unclear how many people are selected, how often promotions are sent, whether the system specifically identifies people with gambling disorders, and what effect the offers have on subsequent betting or losses. The EFF says problem gamblers are highly likely to be targeted, but the provided source does not supply a measurement or methodology for that claim.
Responses and Safeguards to Watch
Further reporting or a statement from DraftKings could clarify the model’s data inputs, targeting criteria, customer safeguards and scale. The source material does not identify a company response, regulatory inquiry, or announced policy change, so no specific next milestone is confirmed.
For now, the EFF points readers to its Surveillance Self Defense resources and advice on protecting data in mobile apps and websites. The group says the case supports broader limits on behavioral advertising; whether lawmakers or regulators act on that position remains unclear.
Key Questions
What does the EFF say DraftKings is doing?
The EFF says DraftKings uses customer betting records to train a machine-learning model that identifies likely losing gamblers, then targets selected customers with promotions encouraging them to return and bet.
What is the source for the report?
The EFF’s September 2026 account attributes the description of DraftKings’ model to The New York Times. The source material provided here does not include the Times article itself or a response from DraftKings.
Does the account say DraftKings buys data from brokers?
No. The EFF says DraftKings appears to use first-party data collected directly from its customers. The material does not describe the model’s full data inputs.
How many customers have been targeted?
The source material gives no customer count, targeting rate or time period for the promotions. Their scale is not established in the account.
What does the EFF want policymakers to do?
The EFF argues for a ban on online behavioral advertising. That is the group’s policy position; the source does not report that such a ban has been adopted in response to the DraftKings report.
Source: hn
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