1. Promotion of Innovation:
– Principle: Ensure minimal regulatory barriers for small and medium-sized AI businesses.
– Action: Regularly review regulations to ensure they don’t stifle innovation.
2. Open Ecosystem:
– Principle: Maintain an “open world” for B2C and B2B AI platforms.
– Action: Encourage open-source sharing and allow AI models to be deployable and adaptable on private servers.
3. Data Management and Privacy:
– Principle: Prioritize user data privacy and respect.
– Action: Implement a “do not train” option for specific data inputs. Allow users to opt out or back into data usage.
4. Attribution and Credibility:
– Principle: Ensure AI systems attribute sources or data analogous to referencing.
– Action: Integrate mechanisms where AI can credit data sources, akin to a search engine.
5. Regulatory Oversight:
– Principle: Areas affecting life, health, or freedom need stringent oversight.
– Action: Collaborate with regulatory bodies in critical sectors to create AI-specific guidelines, drawing inspiration from bodies like the FDA.
6. Access Control and Responsibility:
– Principle: AI systems should implement robust roles and permissions similar to what’s seen in REST APIs.
– Action: Prioritize the development of AI architectures that inherently manage and respect access rights, ensuring the correct data is accessible to the right entities at the appropriate times.
7. Industry Structure and Competition:
– Principle: Recognize potential for both large-scale monopolies and niche specializations in AI.
– Action: Implement policies to level the playing field, encouraging both broad AI advancements and specialized solutions.
8. Government Involvement:
– Principle: While government support can be beneficial, over-involvement can be detrimental.
– Action: Engage in dialogue with government bodies to ensure a balanced approach, preventing potential misuse in politically extreme climates.