Blockchain
Decentralized AI: Leveraging blockchain for a more equitable future
Disclosure: The views and opinions expressed herein are solely those of the author and do not represent the views and opinions of the crypto.news editorial.
Artificial intelligence (AI) is advancing rapidly, but its development and deployment is 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 critical that we 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 companies with access to large amounts of data and computing power dominate the current AI landscape. This centralization presents several problems. Privacy concerns arise because users’ personal data is often collected and used without explicit consent, leading to potential abuse and breaches. The monopolization of power by a few entities stifles innovation and limits diverse contributions. Furthermore, centralized AI systems are vulnerable and can be manipulated for malicious purposes, such as spreading disinformation or conducting surveillance activities.
The reality of AI development today is that it is not just the result of autonomous machine learning, but rather a blend of reinforcement learning and human intelligence. A prime example of this was when the details of Amazon’s “Just Walk Out” technology were detailed I came to the light. Instead of counting customer purchases via technology alone, around 1,000 real people manually tracked sales. This collaboration between human intelligence and AI systems is often overlooked, but it highlights the significant human element in AI processes.
Blockchain technology, with its decentralized and transparent nature, can address these challenges effectively. Improves security and privacy by enabling secure data sharing and storage through cryptographic techniques, ensuring users maintain control over their information. By distributing power across a network, blockchain reduces the risk of monopolization and promotes a more collaborative AI development environment. It can also trace 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 unauthorized AI. Malicious use includes the intentional exploitation of powerful AI to cause widespread harm, such as engineering new pandemics or using AI for propaganda, censorship, and surveillance. The risk of the AI race involves companies or nation-states competing to quickly build more powerful systems, taking unacceptable risks in the process. Organizational risks include major industrial accidents and the possibility of powerful programs being stolen or copied by malicious actors. Finally, there is the risk of rogue AI, where systems might optimize imperfect goals, stray from their original goals, become power-hungry, resist closure, or engage in deception.
Regulation and good governance can contain many of these risks. Malicious use can be addressed by limiting queries and access to various features, and the justice system can hold developers accountable. The risks of rogue AI and organizational issues can be mitigated with common sense and by promoting a security-conscious approach to using AI. However, these approaches do not address some of the second-order effects of AI, such as centralization and perverse incentives left over from legacy web2 companies.
For too long we have traded our private information for access to tools. While deactivation is possible, it is often inconvenient for most users. Artificial intelligence, like any other algorithm, produces results directly tied to the data it is trained on. Significant resources are already allocated to cleaning and preparing data for artificial intelligence. For example, OpenAI’s ChatGPT is trained on hundreds of billions of lines of text from various sources, but it also relies on human input and smaller, custom databases to optimize its output.
Creating a blockchain layer in a decentralized AI network could mitigate these issues. We can build AI systems that track data provenance, maintain confidentiality, and allow individuals and businesses to charge for access to their specialized data using decentralized identity, validation staking, consensus, and roll-up technologies such as optimistic and zero-knowledge proofs. This could shift the balance away from large, opaque, centralized institutions and provide individuals and businesses with an entirely new economic system.
On the technology front, it is crucial to ensure the integrity, ownership and legitimacy of the data (model audit). Blockchain can provide an immutable audit trail for data, ensuring its authenticity and enabling fair compensation for data providers. Techniques like zero-knowledge proofs and decentralized identities allow users to provide data without compromising confidentiality. Decentralized AI networks allow different stakeholders to participate in AI development, from data providers to infrastructure operators, creating a more equitable ecosystem.
In addition to improving data integrity, decentralized AI systems offer greater security. Cryptographic techniques and security protection certification systems ensure that users can protect their data on their devices and control access to their data, including the ability to revoke access. This is a significant advance over the existing system, where valuable information is simply collected and sold to centralized AI companies. Instead, it allows for broad participation in AI development.
Individuals can engage in various roles, such as creating AI agents, providing specialized data, or offering intermediary services such as data labeling. Others could contribute by managing the infrastructure, operating the nodes, or providing validation services. This inclusive approach enables a more diverse and collaborative AI ecosystem.
Decentralized AI also addresses the problem of job displacement caused by advances in AI. As AI systems become more capable, they are likely to have a significant impact on the job market. 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 companies.
Additionally, the integration of blockchain and AI can drive innovation by promoting open source development and collaboration. Decentralized platforms can serve as a foundation for the development of 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 between blockchain and artificial intelligence represents a significant advancement in how we approach technological development. It shifts the balance of power away from centralized entities and towards a more distributed and collaborative model. This transition is essential to ensure 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 trustworthiness of blockchain technology, we can build a more equitable, secure, and innovative AI ecosystem that benefits everyone.
Jiahao Sun
Jiahao Sun, founder and CEO of FLock.io, is an Oxford alumnus and expert in artificial intelligence and blockchain. With previous roles as director of artificial intelligence for the Royal Bank of Canada and AI researcher at Imperial College London, he founded FLock.io to focus on privacy-focused AI solutions. Through his leadership, FLock.io is pioneering advances in the training and deployment of safe, collaborative AI models, demonstrating its dedication to using technology for social progress.