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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 target them with promotions. The account is based on reporting by The New York Times; details about the model, the promotions and their effects have not been independently established in the supplied 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 intended to bring them back to the gambling platform. The EFF’s account, which cites reporting by The New York Times, describes a use of customer data that could expose people experiencing gambling-related harm to further inducements.
According to the EFF, DraftKings analyzes its customers’ betting records to find users it considers likely to lose. The company then directs advertising or promotional offers at those users to encourage them to return and place more bets. The EFF says the promotions are designed to appeal to customers whom the model predicts will make losing wagers. The supplied account does not describe the model’s inputs beyond betting records or explain how DraftKings defines a likely losing bettor.
The EFF says the people the system may reach include problem gamblers, a term it uses for people who repeatedly gamble despite harm to their well-being, finances or relationships. It argues that encouraging such users to gamble again could capitalize on vulnerability. That is the advocacy group’s assessment of the potential harm; the supplied material does not provide data showing how many customers were targeted, how many had gambling problems or what happened after they received promotions.
The EFF characterizes the information used as first-party data, meaning data DraftKings collects directly from its own users. It says this suggests the targeting does not depend on buying additional personal information from outside data brokers. The distinction matters because restrictions focused only on third-party data sales would not, by themselves, address a model built on a company’s own customer records.
How Targeted Offers Could Affect Gamblers
The reported practice links personal betting histories to decisions about who receives gambling promotions. If the EFF’s description is accurate, people predicted to lose could receive marketing intended to prompt additional betting. That creates a potential conflict between customer welfare and commercial incentives: the EFF says losing gamblers generate revenue for DraftKings, while the company’s targeted offers may encourage them to continue.
The account also points to a wider question about data protection. A company may be able to make sensitive predictions from information it already holds, even when it does not purchase outside data. Rules that address only data brokerage could leave this kind of internal profiling untouched. The EFF argues for banning behavioral advertising; that is its policy position, and the report does not establish that such a ban is the only available response.
For readers, the immediate concern is whether gambling promotions are being directed toward people at risk of harm, and whether there are safeguards that prevent or limit that targeting. The supplied source does not answer those questions or quantify any effects on customers.
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Customer Records Power the Model
Behavioral advertising selects or personalizes promotions based on information about a person’s activity. In the example described by the EFF, the relevant activity is betting on DraftKings. The organization says machine-learning tools can process large data sets quickly and use patterns in those records to help select audiences for marketing.
The EFF also raises broader concerns about the data economy, saying information gathered for ad targeting can circulate beyond its original purpose. It points to reporting on data used by insurers, banks and government agencies, and notes that U.S. Immigration and Customs Enforcement issued a request for information seeking details about commercial big-data and ad-tech providers. Those concerns provide wider context, but the supplied material does not say DraftKings shared customer betting records with those entities.
The EFF has long advocated a ban on behavioral advertising. Its article presents DraftKings as an example of how personalized marketing can be applied in a high-risk setting. The account relies on The New York Times’ reporting for the description of the company’s model; no direct response from DraftKings appears in the material provided.
““first party data””
— Electronic Frontier Foundation, describing the data DraftKings uses
Model Reach and Safeguards Remain Unclear
The supplied report does not state how many customers the model identifies or targets, how long the practice has been in use, or what kinds of promotions are sent. It also does not explain the model’s accuracy, the criteria used to classify likely losing bettors, or whether customers can opt out of this targeting.
The material does not include DraftKings’ response, the company’s own explanation of the system, or evidence measuring whether targeted customers placed more bets or experienced greater harm. The EFF’s account cites The New York Times, but the underlying reporting is not reproduced in the supplied source. Those gaps limit what can be concluded about the system’s operation and effects.
Company Response and Policy Questions
The next developments to watch are whether DraftKings comments on the reported model and whether additional reporting clarifies its targeting criteria, safeguards and scale. Details about customer controls, promotion practices and the use of betting records would help establish how the system works in practice.
Policymakers may also face questions about whether rules should cover companies’ use of their own customer data, not only the sale or sharing of information by third parties. The EFF is urging a ban on behavioral advertising, but the supplied material does not identify a specific pending proposal or legislative timetable. For people seeking practical data-protection guidance, the EFF points to its Surveillance Self-Defense resources and advice for mobile apps and websites.
Key Questions
What does the EFF say DraftKings is doing?
The EFF says DraftKings trains a machine-learning model on customer betting records to identify people likely to lose bets, then targets them with promotions intended to bring them back to the platform.
What information is reportedly used?
The EFF says the model uses first-party data collected directly from DraftKings users, particularly their betting records. The supplied material does not specify all data fields used.
Has the effect on customers been established?
No effects are quantified in the supplied account. It does not say how many customers received promotions or whether the targeting led to more betting or increased harm.
Has DraftKings responded?
No response from DraftKings appears in the source material provided. Its explanation of the model and any safeguards remain unclear.
Source: hn
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