DeepProve: Cryptographic Mathematics Guards AI Fairness!
In high-impact fields such as healthcare, finance, and recruitment, AI has begun to deeply participate in decision-making processes.
However, a core challenge remains unsolved: how to prove that it maintains fairness when handling different populations without disclosing the model's details?
Lagrange's DeepProve technology provides a realistic path. It combines zero-knowledge proofs (ZKP) with verifiable computing architecture, allowing AI to generate a mathematical proof while outputting results.
This proof is validated by independent nodes, ensuring the model adheres to established fairness rules without exposing parameters, training data, or internal algorithm logic.
Technically, DeepProve relies on ZK co-processors + a decentralized proof network:
Off-chain execution of high-performance AI inference to ensure computational efficiency;
Generating zero-knowledge proofs to guarantee the immutability and verifiability of the inference process;
On-chain verification, allowing any third party to independently review the fairness and accuracy of the results.
The advantage of this model lies in its balance of privacy, security, and transparency, meeting the requirements of ethical AI while providing technical support for regulatory compliance. Whether hospitals are validating the fairness of drug recommendation models or financial institutions are reviewing credit decision algorithms, DeepProve can provide trustworthy evidence.
Economically, the $LA tokens run through the governance, staking, and incentives of the network, ensuring the activity and security of verification nodes. Stakers can participate in proof generation tasks and earn rewards, thus forming a closed loop of technology and economy.
The future of verifiable AI is not just about making machines smarter; it is also about ensuring they can withstand the scrutiny of cryptographic mathematics on "how to treat everyone."
