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The Electronic Frontier Foundation says DraftKings uses a machine-learning model trained on customers’ betting records to identify losing bettors and send them promotions. The EFF argues this could expose people experiencing gambling-related harm to more inducements, but the provided material does not include a response from DraftKings or detailed evidence about the model’s operation.
The Electronic Frontier Foundation says DraftKings uses a machine-learning model trained on customers’ betting records to identify people who are losing bets and send them targeted promotions to return to the platform. The EFF, citing reporting by The New York Times, says the practice could reach people experiencing gambling-related harm; details about the model and DraftKings’ response were not included in the supplied report.
According to the EFF’s account of the Times report, DraftKings analyzes customers’ betting histories to find users likely to lose, then directs gambling promotions to those customers. The EFF says the company expects these users to place more bets after receiving the promotions. The material provided does not specify how the model defines a losing bettor, how many customers have received such ads, or what promotional offers are used.
The EFF describes the practice as behavioral advertising: personalizing ads based on information collected about a person. It says DraftKings appears to rely on first-party data—information collected directly from its own users—rather than purchasing additional data from outside brokers. That characterization matters because rules focused only on third-party data sales would not necessarily cover targeting built from a company’s own records.
The EFF argues that people it describes as problem gamblers—those who continue gambling despite harm to their finances, relationships or well-being—may be among those the model identifies. That is the advocacy group’s assessment of the risk; the supplied material does not establish how DraftKings classifies customers or whether the system specifically identifies people with a gambling disorder.
Promotions May Reach Vulnerable Bettors
The reported practice connects a customer’s betting outcomes to decisions about who should receive further gambling promotions. If the model identifies people who repeatedly lose and prompts them to return, the targeting may intensify exposure to gambling offers among customers already experiencing harm. The EFF says this creates a business incentive to re-engage losing bettors, but the supplied account does not provide financial records or data measuring the effect of the promotions.
The case also illustrates a gap in potential privacy rules. Because the EFF says the model relies on data DraftKings collects directly, limits on data brokers or third-party transfers alone might not prevent this kind of personalization. The EFF is using the report to renew its call for a broader ban on behavioral advertising, a policy position rather than a description of current law.
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Betting Records Power Ad Targeting
Behavioral advertising uses information about people’s activity to select or personalize the ads they see. In the DraftKings example described by the EFF, the relevant information is reportedly customers’ own betting records. The EFF argues that machine learning can process large datasets quickly and that the difficulty of interpreting how models make decisions can encourage companies to collect more information. The supplied material does not detail which data fields DraftKings uses or how long it retains them.
The EFF also places the case within a wider debate over the commercial use of data gathered for advertising. It says information from the ad-tech industry can reach organizations including insurers, banks and law enforcement agencies. It cites an earlier Immigration and Customs Enforcement request for information about commercial big-data and ad-tech providers. That broader concern is separate from the specific allegation about DraftKings, which the EFF says involves first-party data.
“DraftKings is using its customers’ betting records to train a machine learning model to find losing gamblers.”
— Electronic Frontier Foundation
Model Details Remain Unreported
The supplied account does not include DraftKings’ response, technical documentation, or independent verification of the model’s performance. It does not say how many customers were targeted, how the system determines that a bettor is likely to lose, whether users can opt out, or whether safeguards are applied to customers who may be at risk. The EFF’s concern that problem gamblers could be targeted is an assessment of potential impact; the material does not quantify how often that occurs.
Company Response and Safeguards
The next developments to watch are whether DraftKings responds to the reported use of betting data, and whether further reporting clarifies the model’s criteria, reach and safeguards. The supplied material does not identify a pending regulatory action or a scheduled policy decision. The EFF points readers to its Surveillance Self Defense resources and guidance for limiting data collection by mobile apps and websites.
Key Questions
What does the EFF say DraftKings is doing?
The EFF, citing The New York Times, says DraftKings uses a machine-learning model trained on betting records to find losing bettors and send them promotions to return.
Does the report show that DraftKings targets people with a gambling disorder?
No specific classification process is described in the supplied material. The EFF says people experiencing gambling-related harm may be highly likely to be targeted, but the account does not show how the model identifies such people.
What data does the model reportedly use?
The EFF says DraftKings appears to use first-party data collected directly from its customers, particularly their betting records. The specific data fields are not listed.
Has DraftKings responded to the claims?
The supplied source material does not include a DraftKings response. It also leaves the model’s reach, safeguards and effectiveness unclear.
What policy does the EFF support?
The EFF argues for a ban on online behavioral advertising. That is the organization’s recommendation; the source does not describe it as current law.
Source: hn
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