Definition ∞ Model Corruption refers to the malicious alteration or manipulation of an artificial intelligence or machine learning model, leading to biased, inaccurate, or harmful outputs. In the context of decentralized systems, this can occur if untrustworthy participants inject compromised data during training or update processes. Such corruption compromises the reliability and trustworthiness of the model, potentially affecting applications that rely on its predictions or decisions. Safeguarding against this is crucial for the integrity of AI systems.
Context ∞ Preventing model corruption is a critical challenge for decentralized AI and machine learning initiatives, especially those operating on blockchain networks where data sources can be diverse and trust assumptions varied. Researchers are actively developing robust cryptographic techniques and consensus mechanisms to detect and resist such attacks. Ensuring the integrity of shared models is paramount for the secure and ethical deployment of AI in decentralized environments.
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