Open Source vs Proprietary AI

At a discussion we held in June last year, someone threw the cat among the pigeons by saying that Open Sourcing AI is like giving everyone a nuclear bomb. It’s not a point of view I agree with, given the enablement that generative AI tools provide, but it is a complex issue, with a spectrum of benefits and challenges.

  1. Democratization vs. Monopolization: Open-source AI democratizes access to AI capabilities and spurs innovation, but can also expose AI models to misuse and security vulnerabilities. Closed-source AI safeguards against misuse and protects commercial intellectual property, but it risks concentrating power within high-resource organizations, leading to potential monopolistic consequences. The question arises: How can we strike a balance here?
  2. Security Considerations: Open-source AI, while promoting innovation, presents challenges in patching vulnerabilities, leaving the AI system potentially unsecured. Conversely, closed-source AI allows for identified vulnerabilities to be fixed and safety features to be implemented. The question: How can we ensure the security of AI systems while promoting innovation?
  3. Bias and Performance Disparities: Open-source AI enables the study of risks that can reduce bias and disparate performance for marginalized populations. Essentially, these systems get stress-tested. However, the question remains: How can we ensure that closed-source AI doesn’t inadvertently perpetuate bias and performance disparities?
  4. Future Capabilities: Closed-source AI safeguards against potentially harmful future capabilities. The question then becomes: How can we ensure that open-source AI doesn’t inadvertently lead to the misuse in the future?

The balance between open-source and closed-source AI will continue to be a point of contention, and frankly, we need open-source AI to democratise access, enable development of tools that proprietary AI tools choose not to focus on because of lack of commercial viability. The challenge lies in finding mechanisms for ensuring beneficial usage of AI, and preventing harmful use cases.

Perhaps the only thing more complex than AI itself is deciding how to govern it…