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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 sends them targeted promotions. The account is based on a New York Times report; DraftKings’ criteria, the model’s results and the scale of the practice are not detailed in the supplied source material.
The Electronic Frontier Foundation says DraftKings uses a machine learning model trained on customers’ betting records to identify people it expects to place losing bets, then sends them targeted promotions to encourage more betting. The account, which EFF attributes to a New York Times report, raises questions about how the sportsbook uses customer data to market gambling to people who may be at risk of harm.
According to EFF’s summary of the New York Times reporting, DraftKings analyzes its customers’ betting histories to find users likely to lose. It then directs advertising and promotions at those customers to bring them back to the platform. The source does not specify what promotions were sent, how many people were targeted or over what period.
EFF says DraftKings appears to rely on first-party data—information collected directly from its own users—rather than buying additional data from brokers. EFF characterizes the targeting as a business strategy: customers who lose money generate revenue for the company. That characterization is EFF’s analysis; the supplied material does not include a response from DraftKings or an account from the company explaining the model’s purpose.
The advocacy group argues that people experiencing problem gambling may be especially likely to receive such promotions. It describes problem gambling as repeated gambling despite harm to a person’s finances, relationships or well-being. The source does not establish how DraftKings defines a likely losing gambler, whether the model identifies problem gambling, or whether users can opt out of this targeting.
Promotions May Reach At-Risk Bettors
The report highlights a conflict between personalized marketing and efforts to reduce gambling harm. If a betting company uses customers’ histories to predict who is likely to lose, then uses that prediction to prompt further betting, the same data that supports advertising may also identify people vulnerable to losses. EFF says this approach could expose people already experiencing harm to more inducements to gamble.
The account also raises a policy question: safeguards aimed only at the sale or sharing of data by third parties may not address targeting built from a company’s own customer records. EFF argues that behavioral advertising itself should be banned. That is the organization’s policy position, rather than a description of an enacted rule or a finding by regulators.
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How First-Party Betting Data Fits
Behavioral advertising uses information about people’s activity to personalize the ads or promotions they see. In this case, the data described is customers’ betting records held by DraftKings. EFF says that relying on first-party data shows why limits focused only on data brokers or third-party transfers may leave direct, in-platform targeting untouched.
EFF also warns that machine learning can process large amounts of information quickly and that model decisions may be difficult to interpret. It argues that these features can encourage further data collection. Separately, EFF says data gathered for advertising can flow into other sectors, including insurance, banking and law enforcement. Those broader concerns provide context for its criticism; they do not establish that DraftKings shared this betting data with those entities.
“DraftKings seems to be using solely “first party data” to target their ads”
— Electronic Frontier Foundation
Model Criteria Remain Unreported
The supplied account does not say how many customers were targeted, when the system began operating, how often promotions are sent or what betting patterns the model treats as signs that a customer will lose. It also does not establish whether DraftKings labels users as problem gamblers, whether the model is designed to detect gambling-related harm, or how accurate its predictions are.
DraftKings’ response is not included in the source material, so the company’s explanation of the model and its safeguards is unknown. The account also does not specify whether customers can refuse personalized promotions or whether any regulator has reviewed the practice.
Company and Regulatory Responses
Further reporting or a statement from DraftKings could clarify the model’s purpose, the data it uses, the customers who receive promotions and what controls are available to them. The supplied source does not identify a planned company announcement, regulatory inquiry or policy change, so no next milestone is confirmed.
EFF is urging policymakers to restrict behavioral advertising and points readers to its Surveillance Self Defense resources for guidance on protecting personal data. Whether lawmakers or regulators act on the report remains unclear.
Key Questions
What does EFF say DraftKings is doing?
EFF says DraftKings uses a machine learning model trained on customers’ betting records to identify people it expects to lose and sends them targeted promotions to encourage more betting. EFF attributes the account to a New York Times report.
What information does the model reportedly use?
EFF says DraftKings appears to use first-party data collected directly from its customers, including betting records. The source does not provide a full list of data inputs.
Does the report establish that the model identifies problem gamblers?
No. EFF says people experiencing problem gambling may be especially likely to be targeted, but the supplied account does not establish that DraftKings’ model classifies users as problem gamblers or explain its criteria.
Has DraftKings responded or can customers opt out?
The supplied source material contains no response from DraftKings and does not say whether users can opt out of personalized promotions. Those details remain unclear.
Source: hn
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