FTC action against Match and OkCupid for deceiving users, sharing personal data

FTC Complaint & Settlement

• OkCupid allegedly shared nearly 3M user photos plus demographic and location data with a third party without telling users, contradicting its own privacy policy.
• The proposed settlement mainly prohibits future misrepresentation of privacy policies and data use; several commenters see this as a weak “don’t do it again” response after many years.
• Some wonder if unlawfully transmitted data and any AI models trained on it must be deleted; the thread finds this unclear from the documents.
• Others note this is effectively a “first strike” that sets up harsher penalties for repeat offenses.

Third-Party Data Recipient (Clarifai)

• The FTC complaint identifies the third party as an AI image-recognition company that requested OkCupid data because founders were investors.
• Commenters highlight the lack of contractual limits on data use.
• There is debate over whether the core concern is privacy, potential military applications (e.g., targeting), or both.

Legal & Enforcement Questions

• Some see potential for class actions, including theories around copyright violations.
• Others counter that typical user agreements grant broad licenses and allow sublicensing, though one comment notes this arrangement did not go through formal sublicensing.
• A recurring theme is that large corporations are treated leniently by regulators compared to individuals.

Dating App Business Models & Incentives

• Several comments argue dating apps have misaligned incentives: success means losing paying users, so platforms benefit from keeping people single and engaged.
• This is framed as a reverse network effect: attractive/relationship-ready users churn out, leaving a progressively worse pool.
• Alternative incentive structures are debated (matchmaker-style fees, relationship-based payments, government “dating tax,” charity pledges), but practical and abuse issues are raised.

Pricing, Gender Imbalance & User Experience

• OkCupid’s gender-based pricing (charging different amounts to men vs women) is discussed.
• Some see it as a rational lever to correct severe gender imbalances; others see it as misleading when most “matches” may be bots or low-quality accounts.
• Analogies are drawn to nightclubs admitting women for free to attract men.

Data Analytics & Privacy Expectations

• OkCupid’s historic data analysis and NLP on messages is recalled: ranking reply behavior, studying message patterns, and publishing blog posts/books with findings.
• Some view this as legitimate, anonymized data science to improve matching and provide evidence-based dating advice.
• Others find it troubling given later revelations about undisclosed third-party sharing, and question whether “anonymization” meaningfully protects users.

Fake Profiles, Spam & Security Concerns

• Multiple anecdotes describe account mix-ups, hacked/merged profiles, and spam emerging after account deletion or unique-email registration.
• There is strong suspicion—supported by one claimed industry insider—that many dating platforms use fake female profiles and dark UX patterns to keep men paying and engaged.
• Others point to independent scam operations and chatbots (e.g., pig-butchering scams) as another major source of fake activity.

Broader Reflections on Modern Dating

• Long subthreads explore why online dating feels worse now: hookup vs relationship mismatches, conflicting expectations around first-date spending, social media–driven status signaling, and gendered double standards.
• Commenters differ on whether problematic behaviors are mostly male, female, or systemic; several stress that adversarial mindsets and unrealistic expectations on both sides erode trust and outcomes.