The post Ethereum Co-Founder Suggests Blockchain and ZK-Proofs for Fairer X Algorithms appeared on BitcoinEthereumNews.com.

Vitalik Buterin proposes using blockchain and zero-knowledge proofs to ensure X’s content-ranking algorithm is fair, transparent, and resistant to censorship. This approach would timestamp interactions on-chain and delay code publication, allowing verification without revealing sensitive details. Blockchain timestamps for all content, likes, and retweets to prevent server-side manipulation or censorship. ZK-proofs enable verifiable fairness in algorithmic decisions without exposing underlying data. Platform commits to releasing full algorithm code after a 1-2 year delay, promoting long-term trust; European Commission data shows non-compliance fines exceeding 120 million euros for transparency failures. Vitalik Buterin urges blockchain for fair X content ranking using ZK-proofs. Discover how crypto can enhance social media transparency and combat censorship—explore the full proposal now. What is Vitalik Buterin’s Proposal for Blockchain in Social Media Content Ranking? Vitalik Buterin’s blockchain proposal for social media focuses on integrating zero-knowledge proofs and blockchain technology to create a verifiable, censorship-resistant content-ranking system on platforms like X. In a recent post, the Ethereum co-founder highlighted the need for transparency in algorithms that determine content visibility, arguing that current systems under Elon Musk’s leadership risk amplifying hate while undermining free speech. By using ZK-proofs, platforms could demonstrate adherence to fairness rules without disclosing proprietary details, fostering greater user trust. How Do Zero-Knowledge Proofs Ensure Algorithmic Fairness? Zero-knowledge proofs, a cornerstone of advanced cryptography, allow one party to prove the validity of a statement without revealing underlying information. In Buterin’s vision, ZK-proofs would verify that X’s content-ranking algorithm follows predefined fairness constraints, such as equal treatment of viewpoints or avoidance of bias amplification. For example, the proof could confirm that decisions prioritize educational content without favoring specific ideologies, all while keeping the algorithm’s inner workings private initially. Ethereum Foundation AI lead Davide Crapis echoed this by emphasizing the need for disclosed optimization targets that users can…

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Filed under: News - @ December 15, 2025 4:37 pm