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In today’s world, where AI-driven systems are becoming increasingly central to decision-making and content creation, problems like hallucinations and biases in AI outputs are drawing significant concern. When an AI model generates false or misleading information (a “hallucination”) or reflects societal biases in its outputs, the consequences can be severe — from misinformation and ethical concerns to impacting real lives and decisions.
One emerging approach to address these issues is through Decentralized Knowledge Graphs (DKGs). By combining blockchain technology with verified and diverse data sources, DKGs offer a robust and transparent way to improve the reliability and fairness of AI models.
Introduction to Decentralized Knowledge Graphs (DKGs)
Decentralized Knowledge Graphs (DKGs) are an advanced, blockchain-powered system that facilitates data storage, retrieval, and verification across a decentralized network. Unlike traditional knowledge graphs, which rely on…
