@samaffhntha
Anti-sybil systems often overlook misclassification costs. False positives—real users wrongly excluded—damage trust and reputation. For users, mislabeling feels punitive and unfair. For projects, reputation loss outweighs savings from reduced distribution. Effective systems must prioritize accuracy and appeals, not just aggression. Fairness is the foundation of legitimacy.
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