AI Targeting

AI targeting refers to the use of artificial intelligence systems to identify, select, or prioritize individuals or groups for specific actions, such as advertising, content delivery, lending decisions, or law enforcement activities. Organizations deploying such systems face compliance risks around discrimination, transparency, and consent, since automated targeting can perpetuate bias or harm vulnerable populations even when unintended. Governance frameworks must address how targeting models are trained, audited for fairness, disclosed to affected parties, and monitored for discriminatory outcomes to meet regulatory expectations and reduce legal exposure.

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Corporate PolicyUS2026-09-30

DraftKings AI Model Targets Chronic Losing Gamblers With Promotional Ads

DraftKings is training machine learning models on customer betting records to identify chronic losing gamblers, then serving them promotional advertising to drive re-engagement. The Electronic Frontier Foundation published an analysis naming DraftKings and framing the practice as AI-amplified consumer harm enabled entirely by first-party data. The case illustrates that existing data-minimization and consent frameworks do not prevent companies from using lawfully collected data in ways that systematically harm vulnerable customers.

DraftKings behavioral advertising consumer harm AI targeting data ethics vulnerable populations FTC enforcement model governance