I've been thinking about AI trust a lot lately, mostly because I keep watching projects promise transparency while the actual verification logic stays vague, so when I started reading through how OpenLedger's Proof of Attribution actually distributes $OPEN rewards, I expected the usual staking-and-voting setup — but it's different, and the difference is what bothers me. The rewards go to contributors whose data has the most influence on model outputs, not necessarily the most accurate data. So I'm sitting there reading the Datanet docs and I realize — high influence and high quality are not the same thing. A contributor who uploads data that reinforces a model's existing bias will score well on attribution. A contributor who uploads genuinely corrective data might not move the needle enough to be rewarded. Everyone assumes on-chain verification means the data gets checked for truth, but what OpenLedger actually records is provenance and impact, not correctness. The immutable ledger proves who contributed, not whether what they contributed was right. And I'm not convinced that distinction gets fixed by community flagging alone — the part that still sits with me is whether a system that pays for influence can ever be structurally neutral about what kind of influence it rewards.
Αποποίηση ευθύνης: Περιλαμβάνει απόψεις τρίτων. Δεν παρέχονται συμβουλές. Το Binance AI μπορεί να χρησιμοποιηθεί χωρίς εγγύηση.Δείτε τους Όρους και προϋποθέσεις.
