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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 and details about the model’s operation are not included in the material provided.
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 platform. The EFF’s account cites reporting by The New York Times; the source material does not include a response from DraftKings or independently establish how the model operates.
According to the EFF’s summary of the Times report, DraftKings analyzes users’ betting histories to find customers it expects to lose money. It then directs advertising or promotions to those users to encourage additional betting. The EFF argues that this creates a conflict between a betting company’s commercial interest in customer activity and the risks faced by people whose gambling may be causing harm.
The EFF says DraftKings appears to rely on first-party data—information collected directly from its own customers—rather than buying additional personal data from outside brokers to train the model. The source does not specify which betting records or other data points are included, how the system classifies users, or what kinds of promotions are sent.
The organization says people it describes as problem gamblers—those who repeatedly gamble despite harm to their finances, relationships or well-being—could be among those targeted. That is the EFF’s characterization of the potential impact; the material does not provide figures for how many people are affected or evidence about the model’s accuracy.
Promotions May Reach At-Risk Bettors
If the reported practice is accurate, it raises questions about how betting platforms use customer data to drive repeat play. Promotions aimed at people predicted to make losing bets could increase exposure to gambling for some customers, including people already experiencing harm. The available material does not quantify any resulting losses or show that a particular customer was harmed by a promotion.
The EFF also argues that the example challenges policy approaches focused only on the sale or sharing of data by third parties. If a company can build a targeting model using information it collects directly from its own users, restrictions limited to outside data brokers would not address that use. The EFF advocates banning behavioral advertising, a policy position that goes beyond the reported details of DraftKings’ system.
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How Betting Records Fuel Targeted Ads
Behavioral advertising uses information about people’s activity to personalize which ads or promotions they see. In this case, the EFF says the relevant information is customers’ betting history on DraftKings. The organization argues that machine-learning systems can process large datasets quickly, while their complexity can make it difficult to determine which specific data points influence a model’s decisions.
The EFF places the DraftKings report within a wider concern about commercial data collection. It says data gathered for ad targeting can also circulate to other entities, including insurers, banks and government agencies. The source material mentions an Immigration and Customs Enforcement request for information about commercial big data and advertising technology providers, but gives no evidence that DraftKings’ customer data was shared with ICE or any other agency.
The EFF has long opposed behavioral advertising and points readers to its Surveillance Self-Defense resources for ways to protect personal data. Those positions and resources provide the organization’s policy context; they do not independently verify the reported details about DraftKings.
“DraftKings is using its customers’ betting records to train a machine learning model to find losing gamblers.”
— Electronic Frontier Foundation, summarizing The New York Times report
Model Methods and Reach Remain Unknown
The source material does not include DraftKings’ account of the model, its criteria for identifying customers, the number of users it targets, or the volume and type of promotions involved. It also does not establish whether customers can opt out of this targeting or whether safeguards limit promotions to people showing signs of gambling harm.
The claim that the model targets people likely to lose is attributed to the EFF’s summary of The New York Times reporting. The material provides no underlying dataset, model documentation or independent performance assessment. It is also unclear whether the practices described have changed since the reporting or whether regulators have opened an investigation.
Company and Regulatory Responses Awaited
The next developments to watch are any response from DraftKings, further reporting that documents how its targeting system works, and any action by gambling regulators or lawmakers. The source material identifies no announced investigation, enforcement step or scheduled policy decision. Until those details emerge, the reported use of betting records and the scale of any resulting targeting remain matters for further verification.
Key Questions
What does the EFF say DraftKings is doing?
The EFF says DraftKings uses a machine-learning model trained on customers’ betting records to identify people likely to make losing bets and sends those users targeted promotions to encourage more betting. Its account cites The New York Times.
What information is reportedly used?
The EFF says the model appears to use first-party data collected directly from DraftKings customers. The source material does not list the specific records or data points used.
How many people are affected?
The available material gives no number for how many customers may be targeted, and it does not describe the model’s accuracy or reach.
Has DraftKings responded or faced regulatory action?
No DraftKings response or regulatory action is included in the source material. Whether regulators are reviewing the practice is unclear.
Source: hn
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