How to Mitigate risks of generative AI
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Here are some promising ways to mitigate the risks from generative AI: Ensure diverse, unbiased training data: Carefully curating the data used to train the AI can help reduce bias and unfairness in its creations. The data should be diverse, inclusive, and representative of the population. Increase transparency and explainability: Developing techniques to explain how and why the AI makes its generative decisions can make the system more transparent and trustworthy. This is an active area of research. Establish oversight and review processes: Having humans review and evaluate the AI's outputs can help catch unexpected behaviors or creations that could cause issues. The AI's patterns and trends should be monitored on an ongoing basis. Limited autonomy and human control: Generative AIs should have a degree of constraints and human control rather than being fully autonomous. Giving people oversight and the ability to directly influence the AI helps ensure it generates appropriate content. Consider AI augmentation over replacement: Using AI to augment and enhance human creativity rather than replace it may be a good approach. This allows people to remain in the creative process and leverage AI as a tool. However, in some domains like news reports, replacement could work if proper oversight is in place. It depends on the use case. Address risks of advanced AI: Researchers should continue work on aligning machine goals with human values and ensuring any advanced AI systems of the future are grounded and beneficial. Discussions around regulation and governance of advanced AI should start now rather than later. Set and enforce policies: Developing policies that govern proper data use, oversight, transparency, ethics, and control of generative AI can help set the right safeguards and mitigations. These policies should be monitored and updated regularly based on how the technology and application areas develop. Foster interdisciplinary collaboration: Because generative AI has implications spanning ethics, policy, creativity, and more, addressing risks will require collaboration across researchers in diverse domains. A coordinated effort is needed. No approach is perfect, but using a combination of these techniques can help significantly reduce risks from generative AI and maximize the benefits. With proper safeguards in place, these systems have a lot to offer if developed and applied responsibly. But we must start now to ensure oversight and governance keeps up with progress. |