Trustworthy AI refers to artificial intelligence systems designed and operated to be reliable, fair, transparent, and accountable. Such systems adhere to ethical principles, ensuring their decision-making processes are understandable and their outcomes do not perpetuate bias. The objective is to build AI that users can depend on for critical functions.
Context
The development of trustworthy AI is a significant global imperative, particularly as AI systems are integrated into financial markets and digital asset management. Current discussions focus on establishing regulatory frameworks, technical standards for explainability, and robust validation methods to ensure AI applications in this domain are both effective and ethically sound.
This paper provides the first comprehensive categorization of Zero-Knowledge Machine Learning (ZKML), offering a critical framework to advance privacy-preserving AI and model integrity.
Zero-Knowledge Proofs enable Large Language Models to operate with provable privacy and integrity, fostering trust in AI systems without exposing sensitive data.
A novel blockchain-aided framework ensures data integrity and robustness against manipulation in distributed Mixture of Experts models for large-scale AI.
A novel framework integrates zero-knowledge proofs across machine learning operations, cryptographically ensuring AI system integrity, privacy, and regulatory compliance.
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