Decentralized AI: leveraging blockchain for a more equitable future

Artificial intelligence (AI) is rapidly advancing, yet its development and deployment are largely controlled by a few powerful entities. This concentration of power raises significant concerns about privacy, security, and fairness. As AI continues to transform industries and societies, it is crucial to explore solutions that can democratize its benefits and mitigate its risks. Blockchain technology offers a promising path forward by enabling decentralized, transparent, and secure AI systems.

Large corporations with access to vast amounts of data and computational power dominate the current AI landscape. This centralization presents several problems. Privacy concerns arise as users’ personal data is often collected and used without explicit consent, leading to potential misuse and breaches. Monopolization of power by a few entities stifles innovation and limits diverse contributions. Additionally, centralized AI systems are vulnerable to being manipulated for harmful purposes, such as spreading misinformation or conducting surveillance.

The reality of AI development today is that it is not solely the result of autonomous machine learning but rather a blend of reinforcement learning and human intelligence. A striking example of this was when details of Amazon’s “Just Walk Out” technology came to light. Instead of technology alone tallying customers’ purchases, about 1,000 real people manually checked the sales. This collaboration between human intelligence and AI systems is often overlooked, but it underscores the significant human element in AI processes.

Blockchain technology, with its decentralized and transparent nature, can address these challenges effectively. It enhances security and privacy by enabling secure data sharing and storage through cryptographic techniques, ensuring that users maintain control over their information. By distributing power across a network, blockchain reduces the risk of monopolization and fosters a more collaborative AI development environment. It can also track the provenance of data, ensuring its integrity and legitimacy, which is crucial for training reliable AI models.

Decentralization in AI can mitigate several risks associated with the current centralized model. The Center for Safe AI identifies four broad categories of AI risk: malicious use, AI race, organizational risks, and rogue AI. Malicious use includes intentionally harnessing powerful AIs to cause widespread harm, such as engineering new pandemics or using AI for propaganda, censorship, and surveillance. The AI race risk involves corporations or nation-states competing to quickly build more powerful systems, taking unacceptable risks in the process. Organizational risks encompass serious industrial accidents and the potential for powerful programs to be stolen or copied by malicious actors. Finally, there is the risk of rogue AI, where systems might optimize flawed objectives, drift from their original goals, become power-seeking, resist shutdown, or engage in deception.

Regulation and good governance can contain many of these risks. Malicious use can be addressed by restricting queries and access to various features, and the court system can hold developers accountable. Risks of rogue AI and organizational issues can be mitigated by common sense and fostering a safety-conscious approach to using AI. However, these approaches do not address some of the second-order effects of AI, such as centralization and the perverse incentives remaining from legacy web2 companies.

For too long, we have traded our private information for access to tools. While opting out is possible, it is often inconvenient for most users. AI, like any other algorithm, produces results directly tied to the data it is trained on. Massive resources are already devoted to cleaning and preparing data for AI. For example, OpenAI’s ChatGPT is trained on hundreds of billions of lines of text from various sources but also relies on human input and smaller, more customized databases to fine-tune its output.

Creating a blockchain layer in a decentralized AI network could mitigate these problems. We can build AI systems that track the provenance of data, maintain confidentiality, and allow individuals and enterprises to charge for access to their specialized data using decentralized identities, validation staking, consensus, and roll-up technologies like optimistic and zero-knowledge proofs. This could shift the balance away from large, opaque, centralized institutions and provide individuals and enterprises with an entirely new economic system.

On the technological front, ensuring the integrity, ownership, and legitimacy of data (model auditing) is crucial. Blockchain can provide an immutable audit trail for data, ensuring its authenticity and enabling fair compensation for data providers. Techniques such as zero-knowledge proofs and decentralized identities allow users to contribute data without compromising their confidentiality. Decentralized AI networks enable diverse stakeholders to participate in AI development, from data providers to infrastructure operators, creating a more equitable ecosystem.

In addition to enhancing data integrity, decentralized AI systems offer improved security. Cryptographic techniques and security protection certification systems ensure that users can secure their data on their devices and control access to their data, including the ability to revoke access. This is a significant advancement from the existing system, where valuable information is merely collected and sold to centralized AI companies. Instead, it enables broad participation in AI development.

Individuals can engage in various roles, such as creating AI agents, supplying specialized data, or offering intermediary services like data labeling. Others might contribute by managing infrastructure, operating nodes, or providing validation services. This inclusive approach allows for a more diversified and collaborative AI ecosystem.

Decentralized AI also addresses the issue of job displacement caused by AI advancements. As AI systems become more capable, they are likely to impact the labor market significantly. By incorporating blockchain technology, we can create a system that benefits everyone, from data providers to developers. This inclusive model can help distribute the economic benefits of AI more equitably, preventing the concentration of wealth and power in the hands of a few large corporations.

Furthermore, the integration of blockchain and AI can foster innovation by promoting open-source development and collaboration. Decentralized platforms can serve as a foundation for developing new AI applications and services, encouraging a diverse range of contributors to participate in the AI ecosystem. This collaborative environment can lead to the creation of more robust and innovative AI solutions, benefiting society as a whole.

In conclusion, the fusion of blockchain and AI represents a significant advancement in how we approach technology development. It shifts the balance of power away from centralized entities and towards a more distributed and collaborative model. This transition is essential for ensuring that AI serves the broader interests of humanity rather than the narrow goals of a few powerful organizations. The future of AI lies in its decentralization, and blockchain is the key to unlocking this potential. By leveraging the inherent security, transparency, and trustlessness of blockchain technology, we can build a more equitable, secure, and innovative AI ecosystem that benefits everyone.