# Ethereum's Infrastructure is Critical in Building Trust and Safety in AI - Ismael Hishon Rezaizadeh

- Channel: [ETHGlobal](https://streameth.org/ethglobal)
- Date: 2025-10-09
- Duration: 20:38
- Topics: Ethereum, zero knowledge proofs, ZKML, machine learning, AI verifiability, privacy, safety, Deep Prove, Lrange Labs, cryptography, trust, transparency, explainability, fairness, bias, healthcare, defense, finance, content moderation, decentralized, Web3, Web2, compliance, agentic systems, DeFi, governance, scalability, inference, proofs of training, proofs of reasoning, open source, industry adoption, Intel, Nvidia, AWS
- Watch: https://streameth.org/watch/yt-krzeisvzBhA
- YouTube: https://www.youtube.com/watch?v=krzeisvzBhA

## Description

Ismael Hishon Rezaizadeh, co-founder of Lrange Labs, discusses the critical role of Ethereum's infrastructure and zero knowledge proofs (ZKPs) in ensuring trust, privacy, and safety in AI applications. He introduces zero knowledge machine learning (ZKML), which leverages ZKPs to verify AI model integrity, privacy, and fairness, especially in security-sensitive sectors like healthcare, defense, finance, and content moderation.

Lrange’s Deep Prove library enables fast, scalable, and commercially viable ZKML, supporting large models and real-world deployments with partners like Intel, Nvidia, and AWS. Ismael emphasizes the need for verifiability and transparency in AI, moving beyond blind trust to cryptographic proofs, and highlights the growing adoption of ZKML in both Web2 and Web3. He also discusses future advancements, including proofs of training, fairness, and reasoning, and the societal impact of embedding verifiability and privacy as primitives in AI systems.

00:00 Introduction  
00:36 Zero Knowledge Proofs & ZKML Overview  
01:35 Challenges in AI Trust, Safety, and Bias  
02:26 Importance of ZKPs for AI Verifiability  
03:17 Lrange Labs & Deep Prove Introduction  
04:29 How Deep Prove Works  
05:56 Use Cases: Defense, Healthcare, Web2, Web3  
08:05 Decentralization & Open Source Approach  
09:07 Lrange Research & Industry Collaboration  
10:44 Extending ZKML Beyond Crypto  
11:29 Societal and Sectoral Impact of ZKML  
13:14 Agentic Systems, Compliance, Content Moderation  
15:02 DeFi, Governance, and Healthcare Applications  
16:40 The Future: ZK as a Default Primitive  
17:43 Q&A: ZK Computational Improvements & Future Directions  
20:33 Conclusion

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