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AI x Crypto

a16z Podcast

Ensuring Fairness and Transparency in Machine Learning through Zero Knowledge Techniques

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By having banks commit to a model and allowing data assessment by the same model for everyone, it ensures consistency in applying models. Zero knowledge techniques can further verify fairness and model application equality. Algorithmic fairness is crucial to address biases, ensuring fair results in machine learning. Zero knowledge techniques like ZKML can potentially prove model fairness and confirm correct training processes, enhancing transparency and trustworthiness in AI. Decentralizing AI is a long-term goal, and the application of zero knowledge and verification techniques could aid in achieving this by enabling more transparency and trust in machine learning processes.

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