[Paper Review] Is Decentralized AI Safer?

Casey Clifton, Richard Blythman+1 more|arXiv (Cornell University)|Nov 4, 2022

Blockchain Technology Applications and Security4 citations

TL;DR

This paper investigates whether decentralizing AI through blockchain technology enhances safety and ethics by enabling decentralized governance via DAOs, immutable audit trails, and democratized data access. It finds that decentralization can reduce bias and improve alignment with diverse moral perspectives, but introduces new risks like poor accessibility, security vulnerabilities, and data monetization ethics.

ABSTRACT

Artificial Intelligence (AI) has the potential to significantly benefit or harm humanity. At present, a few for-profit companies largely control the development and use of this technology, and therefore determine its outcomes. In an effort to diversify and democratize work on AI, various groups are building open AI systems, investigating their risks, and discussing their ethics. In this paper, we demonstrate how blockchain technology can facilitate and formalize these efforts. Concretely, we analyze multiple use-cases for blockchain in AI research and development, including decentralized governance, the creation of immutable audit trails, and access to more diverse and representative datasets. We argue that decentralizing AI can help mitigate AI risks and ethical concerns, while also introducing new issues that should be considered in future work.

Motivation & Objective

• To evaluate whether decentralizing AI using blockchain technology can improve safety, ethics, and alignment with diverse human values.
• To examine how decentralized autonomous organizations (DAOs) can formalize inclusive governance in AI research and development.
• To assess the role of blockchain in enabling immutable audit trails and secure, representative data sharing for AI models.
• To identify and analyze new risks introduced by decentralization, including accessibility gaps and data security threats.
• To guide future research toward building equitable, auditable, and resilient decentralized AI systems.

Proposed method

• Using case studies and analysis of existing blockchain applications in AI to evaluate use-cases in governance, data access, and auditability.
• Applying the concept of Decentralized Autonomous Organizations (DAOs) to coordinate and incentivize community-driven AI R&D decisions.
• Leveraging blockchain’s immutability to create tamper-proof logs for AI model training, data provenance, and decision-making.
• Analyzing existing data-sharing models that tokenize personal data, enabling secure and incentivized participation in AI training.
• Evaluating technical and social challenges through informal surveys and risk assessments of current decentralized AI implementations.
• Integrating established AI safety techniques such as federated learning and explainable AI to mitigate bias and enhance privacy in decentralized systems.

Experimental results

Research questions

• RQ1 Can decentralized governance via DAOs lead to more ethical and representative AI development than centralized corporate models?
• RQ2 How does blockchain enable more transparent and auditable AI systems through immutable data trails?
• RQ3 To what extent does decentralized data ownership reduce model bias and improve fairness in AI systems?
• RQ4 What new risks emerge from decentralizing AI, particularly in terms of accessibility and security?
• RQ5 How can decentralized AI systems be designed to prevent exploitation of vulnerable populations through data monetization?

Key findings

• Decentralized AI governance through DAOs can align AI development with broader societal values by enabling participatory decision-making, as demonstrated by employee-led ethical actions at Google.
• Blockchain enables immutable audit trails that enhance transparency and accountability in AI model development and deployment.
• Decentralized data sharing can improve dataset diversity and reduce bias, especially when combined with privacy-preserving techniques like federated learning.
• Despite potential benefits, 79% of DAO participants surveyed were men aged 20–40, indicating significant accessibility and diversity gaps in current decentralized governance models.
• End-user security risks are heightened due to misperceptions of blockchain immutability and the potential for fraud in data tokenization, similar to cryptocurrency scams.
• Monetizing personal data via blockchains may exploit vulnerable populations and reinforce existing societal biases, especially when discriminatory variables are not properly controlled.