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The Electronic Frontier Foundation says DraftKings uses a machine-learning model trained on customers’ betting records to identify people it predicts will lose bets and respond to promotions. The account, which cites reporting by The New York Times, raises questions about how the targeting works and what safeguards apply.
The Electronic Frontier Foundation says DraftKings uses a machine-learning model trained on customers’ betting records to identify people it predicts are likely to lose bets and respond to gambling promotions. Citing reporting by The New York Times, the digital rights group says the sportsbook then sends targeted advertising to bring those customers back to place more bets, a practice that raises concerns about marketing to people who may be experiencing gambling-related harm.
In its report, EFF describes DraftKings as using its customers’ betting records to train a model that identifies losing gamblers. The account says the company targets people identified by the model with promotions intended to bring them back to its platform. EFF says the promotions are aimed at customers DraftKings believes will make losing bets. The source material does not provide the model’s technical design, accuracy, or the number of customers affected.
EFF characterizes the data as first-party information: records DraftKings collects directly from its own users. According to the group, DraftKings appears to rely on that information rather than buying additional data from outside brokers to power the model. The report does not provide a statement from DraftKings confirming the practice or explaining how the company uses customer betting histories.
EFF argues that people it describes as problem gamblers may be especially likely to be targeted. It defines problem gambling as repeatedly gambling despite harm to a person’s well-being, finances, or relationships. That concern is the group’s assessment of who could be affected; the supplied material does not establish how DraftKings defines or identifies problem gambling, or whether the model is designed to identify that condition.
Targeted Promotions Raise Gambling Risks
The reported practice matters because it would use a customer’s betting history not simply to tailor ordinary advertising, but to select people a model predicts will continue betting and lose. If promotions bring those customers back, the approach could expose people already facing gambling-related harm to further inducements. The source account does not quantify any resulting losses or establish that the promotions caused harm to particular customers.
EFF frames the issue as a conflict between personalized marketing and harm reduction. The company may have a commercial incentive to retain customers who lose, while those customers may have reasons to stop or limit gambling. The report describes that incentive as EFF’s account of DraftKings’ business model, rather than presenting financial data that measures the model’s effect on revenue.
The case also has implications for privacy policy. If the reported model uses data collected directly by DraftKings, rules focused only on the sale or exchange of third-party data would not address this particular use. EFF says the example supports its broader call to prohibit behavioral advertising. That is the organization’s policy position, not a description of current law or a finding that DraftKings has violated a rule.
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How Betting Records Fuel Ad Targeting
Behavioral advertising uses information about people’s activities to personalize the ads they see. In this case, the source material says DraftKings’ model draws on betting records held by the company. That makes the report an example of how information generated within a service can potentially be used to shape marketing to its own customers, even without data purchased from an outside broker.
EFF says AI can increase the scale and speed of this kind of targeting by processing large data sets and helping companies refine their models. It argues that the difficulty of knowing in advance which data points a model will use can create pressure to gather more information. The supplied account does not document how much data DraftKings collects for this model, whether it uses additional data beyond betting records, or how often the model is updated.
EFF places the DraftKings report within a wider debate over the data gathered for advertising. It says information collected for ad targeting can move into other sectors, including insurance, banking, and government investigations. The group also points to an Immigration and Customs Enforcement request for information about commercial big-data and ad-tech providers. Those broader concerns are EFF’s context for its argument; the account does not say DraftKings shared its customer data with those entities.
“EFF says DraftKings uses customers’ betting records to train a machine-learning model to find losing gamblers.”
— Electronic Frontier Foundation
Model Details Remain Unreported
The source material does not include a direct response from DraftKings. It does not specify how the model classifies customers, what data fields it uses, how the company measures whether someone is likely to lose or respond to an offer, or how accurate those predictions are. It also gives no count of customers targeted, details about the promotions, or estimate of their effect on betting activity.
It remains unclear what protections customers can use to opt out of this targeting, whether the model is subject to internal review, and whether regulators have examined the practice. EFF’s report raises the possibility that people experiencing problem gambling could be reached by the promotions, but the provided material does not establish that the model identifies customers by a clinical diagnosis or that every customer it targets is experiencing gambling harm.
Company and Safeguard Questions
Further clarity would depend on a response from DraftKings or additional reporting that describes the model’s operation and the safeguards around it. Relevant details include what betting information is used, how promotions are selected, whether customers can decline personalized marketing, and what steps the company takes when customers show signs of harm.
EFF is urging policymakers to address behavioral advertising broadly, including targeting based on data collected directly by a company. The source material does not identify a specific pending bill, regulatory decision, or enforcement action tied to DraftKings. The next developments to watch are any company explanation, new reporting on the model, or policy proposals that address personalized gambling promotions.
Key Questions
What does EFF say DraftKings is doing?
EFF says DraftKings uses customers’ betting records to train a machine-learning model and targets customers it predicts will lose and respond to gambling promotions. The group cites reporting by The New York Times.
Is the model confirmed to identify problem gamblers?
The supplied account does not establish that. EFF says people it describes as problem gamblers may be especially likely to be targeted, but it does not explain whether DraftKings’ model identifies a gambling disorder.
What data does the reported model use?
EFF says DraftKings appears to use first-party data, including customers’ betting records, rather than data bought from third parties. The full data inputs have not been detailed in the source material.
Has DraftKings responded to the report?
The source material does not include a response from DraftKings or information about an opt-out process, safeguards, or regulatory review.
Source: hn
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