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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 lose and target them with promotions. The account is based on reporting by The New York Times; DraftKings’ response and details about the model’s operation are not provided in the source material.
The Electronic Frontier Foundation says DraftKings uses machine learning trained on customers’ betting records to identify gamblers likely to lose and send them targeted promotions to return to its sports betting platform. The account, which the EFF attributes to reporting by The New York Times, raises questions about how gambling companies use customer data and whether promotions can reach people experiencing gambling-related harm.
According to the EFF’s account of the Times report, DraftKings analyzes its users’ betting histories to find customers it expects to make losing bets. The company then directs advertising and promotions at those customers, with the aim of bringing them back to place more bets. The EFF says DraftKings uses first-party data—information it collects directly from its users—for this model, rather than buying additional data from outside brokers.
The EFF argues that people it describes as problem gamblers may be especially likely to fall within the model’s target group. It says the system’s reported purpose is to identify likely losing bettors, but the available source does not explain how the model defines or identifies gambling-related harm, or whether DraftKings uses a separate risk assessment.
The EFF frames the practice as behavioral advertising: using information about a person’s activity to personalize ads. It argues that promotions designed to bring back customers expected to lose can exploit vulnerability. That assessment is the advocacy group’s interpretation; the source material does not include a DraftKings statement or independent details about the model’s performance.
Promotions Reach Vulnerable Bettors
If the EFF’s description is accurate, the system links detailed gambling records to personalized efforts to prompt further betting. That matters because people who continue gambling despite financial, personal or relationship harms may be among those the model reaches. The report does not establish that every person targeted has a gambling disorder, but it points to a potential conflict between customer protection and a business incentive to retain bettors who lose.
The episode also shows why limits on third-party data sales alone may not address concerns about targeted advertising. The EFF says DraftKings relies on data it collects itself. Its policy argument is that companies can profile users and personalize promotions without purchasing outside data, leaving direct collection and use of customer records at the center of the issue.
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How Betting Records Fuel Ads
Behavioral advertising personalizes promotions based on information gathered about users. In this case, the EFF says the data comes from customers’ betting activity on DraftKings. The source does not specify which individual data points the company uses, how long it retains them, or what controls customers have over their use.
The EFF places the report within wider concerns about the ad technology industry. It says behavioral data can circulate beyond the company that collected it, including to businesses and government agencies. It cites an earlier U.S. Immigration and Customs Enforcement request for information about commercial big-data and ad-tech providers. That broader concern is separate from the DraftKings account: the EFF says this particular model appears to use data collected directly from DraftKings customers.
“DraftKings is using its customers’ betting records to train a machine learning model to find losing gamblers.”
— The Electronic Frontier Foundation, summarizing The New York Times report
Model Details Remain Unreported
The source material does not include a response from DraftKings or independent confirmation of the model’s operation. It also does not specify when the system was introduced, how many customers it targets, what promotions they receive, or how the company measures whether the model predicts losses. The precise data inputs, retention practices and safeguards are not described.
It is also unclear whether DraftKings distinguishes customers experiencing gambling-related harm from other bettors it predicts will lose. The EFF says such people are likely to be targeted, but the material provides no figures or methodology to quantify that risk. The reported use of betting records to predict losses should not be treated as proof that every targeted customer is a problem gambler.
Responses and Safeguards to Watch
The next key developments would be a response from DraftKings and further reporting on how the model selects customers, what promotions follow, and whether users can opt out. Information about responsible gambling safeguards, their application to people identified by the system, and any regulatory review would help clarify how the practice affects customers.
The EFF is calling for a ban on behavioral advertising and points readers to its Surveillance Self-Defense resources for guidance on protecting personal data. The source material does not identify a pending law, enforcement action or specific policy timetable tied to the DraftKings report.
Key Questions
What does the report say DraftKings is doing?
The EFF says DraftKings trains a machine learning model on customer betting records to identify people likely to lose and then sends them targeted promotions to return and bet again. The EFF attributes this account to The New York Times.
Does the report show that every person targeted is a problem gambler?
No. The EFF says people experiencing gambling-related harm may be especially likely to be targeted, but the source provides no data showing that all targeted customers meet that description.
What data does the model reportedly use?
The EFF says the model appears to use first-party data collected directly from DraftKings customers, including betting records. The specific inputs and how long they are kept are not described.
Has DraftKings responded to the report?
The source material provided for this article includes no response from DraftKings. The company’s position and details about its safeguards remain unclear.
Source: hn
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