> ## Documentation Index
> Fetch the complete documentation index at: https://docs.theblockchainlibrary.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Machine Learning and Blockchain Synergy: Opportunities and Challenges for ML Models and Smart Contracts

> This paper explores the convergence of machine learning (ML) and blockchain technology, focusing on the potential for autonomous AI to govern smart contracts an

# Machine Learning and Blockchain Synergy: Opportunities and Challenges for ML Models and Smart Contracts

This paper explores the convergence of machine learning (ML) and blockchain technology, focusing on the potential for autonomous AI to govern smart contracts an

## Abstract

This paper explores the convergence of machine learning (ML) and blockchain technology, focusing on the potential for autonomous AI to govern smart contracts and the potential for blockchain to provide secure provenance for ML training data. The authors analyze how ML models can be embedded into decentralized applications to automate complex decision-making processes. Conversely, the paper examines how blockchains can address the 'black box' problem in AI by providing a tamper-proof log of model training iterations and decision outcomes. Key challenges addressed include the computational cost of running ML models on-chain (the oracle problem) and the need for zero-knowledge proofs to verify ML computations without revealing sensitive underlying data. The paper proposes a taxonomy of synergy, classifying applications into areas such as fraud detection, autonomous governance, and secure data sharing. The work concludes that while technical obstacles regarding latency and scalability remain, the integration of decentralized computing and AI is vital for creating robust, autonomous digital systems.

**Authors:** M. Shoaib, A. Al-Zahrani

**Publication:** Academic Paper

**Publication date:** 2024-01-01

## Key findings

* Blockchain provides an immutable record for AI data lineage and provenance.
* The computational overhead of on-chain ML requires off-chain compute solutions like ZK-proofs.
* AI can be utilized to optimize smart contract governance and detect security vulnerabilities.
* Decentralized data markets can democratize access to the high-quality datasets needed for ML model training.

## Citation

M. Shoaib, A. Al-Zahrani (2024). Machine Learning and Blockchain Synergy: Opportunities and Challenges for ML Models and Smart Contracts. Academic Paper. [https://www.sciencedirect.com/science/article/pii/S2096720925001381](https://www.sciencedirect.com/science/article/pii/S2096720925001381)

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***

**Canonical knowledge ID:** `research:machine-learning-and-blockchain-synergy-opportunities-and-challenges-for-ml-models-and-smart-contracts`