Ensuring Ethical and Compliant AI Model Training: A Blockchain-based Framework for Copyright Management, Provenance, and Lineage
Основні поняття
A blockchain-based framework, IBIS, that empowers AI model owners to establish provenance and lineage of their AI models and training datasets, efficiently obtain copyright licenses from relevant copyright holders, and securely record and renew bilaterally signed copyright licenses as evidence of legal compliance.
Анотація
The paper presents IBIS, a blockchain-based framework for data and model copyright management, provenance, and lineage in AI model training processes. IBIS addresses key challenges in the AI industry, including:
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Seamless integration with existing AI model training workflows by supporting iterative model retraining and fine-tuning, accommodating diverse copyright agreements through flexible license checks and renewals, and providing a unified API that integrates with existing contract lifecycle management software.
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Adaptability through establishing links between models in the model metadata, and integrating periodic license renewal checks via smart contracts, enabling ongoing model retraining and license renewal.
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Traceable registry by deploying three on-chain, immutable registries for dataset metadata, licenses, and model metadata, maintaining authentic records of dataset and model relationships, ownership, and their copyright agreements.
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Blockchain-based multi-party signing workflows between AI model owners and copyright holders, ensuring the establishment of legally compliant licensing agreements.
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Controllability by implementing on-chain access control mechanisms and adhering to strict permission rules, facilitating a unified platform while safeguarding commercial sensitivity needs.
The framework is implemented using Daml smart contracts on the Canton blockchain protocol, and evaluated for performance and scalability under varying numbers of users, datasets, models, and licenses.
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Is Your AI Truly Yours? Leveraging Blockchain for Copyrights, Provenance, and Lineage
Статистика
The number of models in the model chain (M) and the number of training datasets per model (T) linearly impact the execution time of fetching model licenses and datasets.
The number of scraped datasets per model owner (D), model owners (N), and licenses per model owner (L) do not significantly impact the performance of fetching model licenses and datasets.
The performance of fetching authorized models displays high standard deviations due to the presence of redundancy in licenses and datasets.
Цитати
"IBIS empowers model owners to establish the provenance and lineage of their AI models and training datasets throughout retraining and fine-tuning processes, efficiently obtaining copyright licenses from the relevant copyright holders, and securely recording and renewing bilaterally signed copyright licenses as evidence of legal compliance."
"IBIS exhibits the following characteristics: Seamless integration, Adaptability, Traceable registry, Blockchain-based multi-party signing, and Controllability."
Глибші Запити
How can IBIS be extended to support more complex licensing agreements, such as those involving multiple copyright holders or dynamic pricing models?
To support more complex licensing agreements in IBIS, several enhancements can be implemented:
Multiple Copyright Holders:
Introduce a mechanism to handle licenses involving multiple copyright holders. This could involve creating a structure within the smart contracts to accommodate multiple parties as signatories or controllers.
Implement a multi-signature feature where all copyright holders must sign off on the license agreement for it to be considered valid.
Develop a notification system to alert all copyright holders of any proposed changes or renewals to the license agreement.
Dynamic Pricing Models:
Incorporate smart contracts that can adjust pricing dynamically based on predefined conditions or external factors.
Integrate oracles to fetch real-time data that can influence pricing decisions, such as market trends, demand-supply dynamics, or regulatory changes.
Implement a flexible pricing structure that allows for tiered pricing, discounts, or incentives based on specific criteria.
Automated Negotiation:
Develop an automated negotiation feature that can facilitate discussions between parties to reach consensus on complex licensing terms.
Utilize smart contract logic to enable dynamic negotiation based on predefined rules and parameters set by the parties involved.
Implement a transparent and auditable negotiation process that records all interactions and agreements on the blockchain.
By incorporating these features, IBIS can evolve to handle more intricate licensing agreements involving multiple stakeholders and dynamic pricing models effectively.
