DraftKings Is Using AI to Behaviorally Target Chronic Gamblers
- ID
- 29999
- Status
- summarized
- Published
- 30 Sep 2026, 12:30 AM
- Fetched
- 30 Sep 2026, 4:16 AM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://www.eff.org/deeplinks/2026/09/draftkings-using-ai-supercharge-harms-online-behavioral-advertising
- Source URL
- https://hnrss.org/best
Summary
- Score
- 6.0
- Created
- 30 Sep 2026, 4:17 AM
- Tags
- Audience
- developersai_ml_learnerssaas_founders
What happened
An EFF Deeplinks post (by Devanshi Nishar, dated September 24, 2026) reports, citing the New York Times, that DraftKings trains a machine learning model on customers' betting records to identify gamblers likely to place losing bets, then sends those customers targeted promotions to lure them back to place more bets. EFF frames this as an extreme case of online behavioral advertising and argues that all behavioral advertising should be banned. The Hacker News thread drew 365 points and 240 comments.
Why it matters
This is a concrete example of the label choice doing the harm, not the model: the training signal is customers' own betting records, and the optimization target is 'will place losing bets,' which is why people flagged as problem gamblers get re-targeted. If you ship personalization or recommendation features, the useful takeaway is to name your model's target variable out loud — 'predicted revenue per user' can silently encode the same thing this article describes. Note the text contains no Malaysia- or Southeast Asia-specific detail, so any local regulatory angle would have to come from outside this source.
Discussion angle
Take a real product you've built: what was the model's target label, and would you be comfortable explaining that label on stage? Then ask whether 'maximize predicted revenue' is a defensible label or just a laundering of the DraftKings pattern.