Seeking's Public Scammer Gallery: Transparency or Legal Minefield?

Sugar dating platform Seeking.com has abandoned the industry-standard practice of quietly removing suspicious accounts in favour of publicly displaying photographs of alleged scammers. The company says its AI catches the vast majority of fraudsters before they can message other members, but the shift from invisible moderation to public galleries raises urgent questions about algorithmic accuracy and legal liability. What happens when the algorithm gets it wrong?

The Fraud Pressure Cooker

Romance scam losses reached $1.16 billion in the first nine months of 2025 alone, according to Federal Trade Commission figures. That makes romance fraud one of the fastest-growing scam categories in the US, and it puts dating platforms under extraordinary pressure to demonstrate they're taking action.

Seeking operates in a particularly high-risk segment. Sugar dating platforms, where financial exchange is baked into the relationship model, attract fraudulent activity at higher rates than mainstream apps. Members expect money to change hands. That creates cover for scammers who can operate longer before arousing suspicion.

The company's AI verification system reportedly identifies suspicious accounts during onboarding, before they can initiate contact with other members. Seeking hasn't disclosed the specific signals its models use, but standard approaches include image recognition to spot stock photos or previously flagged content, device fingerprinting to catch serial account creators, and behavioural analysis during profile setup.

What distinguishes Seeking's approach isn't the technology: most major platforms use similar detection tools. It's the decision to make enforcement visible rather than invisible.

Public Shaming as Product Strategy

Traditional trust and safety practice operates quietly. Suspicious accounts get shadow-banned, flagged for human review, or removed without fanfare. The user experience is seamless. Bad actors disappear, ideally without knowing exactly what triggered their removal.

Seeking has inverted that model. By publicly displaying profiles it identifies as fraudulent, the platform signals to paying members that it's actively fighting fraud. The message is clear: we're catching them, and we want you to see it.

When someone's photograph appears in a public gallery labelled as a scammer, the reputational damage is immediate and difficult to reverse.

The company hasn't disclosed its false positive rate. The 90% detection figure, attributed to internal data, tells us nothing about accuracy. Catching 90% of scammers is impressive only if the remaining flagged accounts aren't legitimate users whose photos happened to match patterns the model associates with fraud.

The Legal Exposure

UK data protection law presents a second problem. Publishing photographs and profile information of alleged scammers arguably constitutes processing personal data for purposes beyond the original collection. Under UK GDPR, that requires a lawful basis, and 'we think they're fraudsters' may not meet the threshold.

Legitimate interest could apply if Seeking can demonstrate that public disclosure is necessary and proportionate for fraud prevention. But the Information Commissioner's Office takes a dim view of naming and shaming, particularly when the underlying determination comes from an automated system without meaningful human review.

The platform hasn't specified whether flagged accounts receive notice or opportunity to appeal before their images go public. If they don't, Seeking is essentially acting as investigator, judge, and executioner: a posture that works until someone sues.

This is transparency theatre masquerading as trust and safety innovation.

Other platforms have tested transparency tactics without crossing into public identification. Tinder's photo verification prompts users to submit real-time selfies. Bumble displays verification badges on profiles that pass its checks. Both approaches signal safety without publishing alleged fraudsters' faces.

What Operators Should Watch

The broader industry will be watching whether Seeking faces regulatory pushback or litigation from users claiming wrongful identification. If the company can demonstrate that public galleries measurably reduce fraud without triggering enforcement action from the ICO, expect copycats.

But the risk-reward calculation looks unfavourable. Public shaming might placate members frustrated by scam prevalence, but it exposes operators to defamation claims, data protection fines, and the reputational cost of high-profile false positives. One sympathetic case, a legitimate user whose photo ends up in a scammer gallery, could undo whatever trust the tactic builds.

The more sustainable approach remains what Match Group (MTCH), Bumble (BMBL), and other mainstream operators already do: invest in detection systems, remove bad actors quietly, and communicate enforcement volumes in transparency reports. Members don't need to see the scammers' faces. They need to trust that the platform is catching them.

Seeking's experiment tests whether public accountability can substitute for that trust. The answer will depend less on the AI's accuracy than on whether the first wrongly accused user decides to lawyer up. According to VICE's coverage of the initiative, the platform is blocking the vast majority of scammers before they ever begin messaging members, though the company's Chief Technology Officer has acknowledged romance fraud as a growing problem requiring constant innovation.