Discord has been using ML to determine the gender and age of some of its users
Discord is reportedly using machine learning to infer users’ age and gender from their behavior, raising concerns over privacy, transparency, and compliance with laws like GDPR and California’s data protection rules. Commenters weigh the business incentives—ad targeting, demographic analytics, regulatory pressure to detect minors—against risks such as unwanted profiling, outing sensitive traits, and potential misuse by governments or bad actors, with some advocating open-source or self-hosted chat alternatives as an escape from “enshittification.”
Business and Product Motives
- Many see the main driver as advertising: age/gender inference improves ad targeting, pricing, and partner pitches (e.g., “we have X 18–24-year-olds”).
- Some think Discord is building an ad network / platform, especially after announcing in-app ads and “quests.”
- Demographic inference can also support market research and customer segmentation offerings at different price points (self‑reported vs inferred data).
Regulatory and Child Safety Arguments
- Several argue ML age detection may be used to identify under‑13 or otherwise underage users for compliance with laws like the UK Online Safety Act, EU child‑protection rules, and similar.
- Counterpoint: if the sole aim is age‑gating, you only need “too young vs old enough,” not fine‑grained age bands and gender.
Privacy, Consent, and Legal Concerns
- Strong pushback that users never explicitly gave age/gender to Discord, yet these are being inferred from behavior and text.
- Under frameworks like GDPR/CCPA (as described by commenters), users should know what is collected, how it’s used/shared, and be able to have it corrected or deleted; “inference” is seen by many as equivalent to collecting.
- Disagreement over whether probabilistic scores for gender/age legally count as personal data.
- Some worry that inferred traits (e.g., sexuality, gender) could be dangerous if accessed by governments or hostile actors.
Targeting, Segmentation, and ML Use
- Debate over whether demographic targeting adds value beyond pure behavioral targeting; some argue behavior alone is superior, others say demographics remain a key axis advertisers demand.
- Discussion of “person type” / persona clustering vs explicit demographics; advertisers often still want human‑readable categories.
User Trust, Enshittification, and Alternatives
- Many view this as part of the broader “enshittification” of Discord as it moves to an ad‑driven model.
- Some users discuss migrating to alternatives (Matrix, Revolt, Mattermost, P2P systems) to regain control and avoid surveillance.
Other ML Uses and Concerns
- Reports that Discord also uses ML to infer voice‑channel topics and surface them to others in the server, which some find intrusive.
- A minority defends such ML as necessary for combating child exploitation, scams, and other abuses; others see this as overstated or as cover for monetization.