The article emphasizes the need for human involvement in validating AI results, protecting data privacy, ensuring AI system security, promoting ethical use, providing transparency in AI decision-making processes, and establishing accountability for AI outputs. It also highlights the potential risks of generative AI, such as data leakage and inherent biases in training data. The proposed governance board would be responsible for setting strategic direction, ensuring regulatory compliance, and evaluating technical robustness, among other tasks.
Key takeaways:
- AI governance should include human-centricity in design and oversight, with humans validating AI results either before or after action is taken, or AI operating independently.
- Privacy and data protection are crucial, with enterprises needing to understand what data is used for fine-tuning and embeddings, and how access to this data is controlled and monitored.
- Safety, security and reliability are key, with generative AI security needing to protect against prompt injection, data poisoning, and data leakage.
- Transparency, explainability, accountability and liability are also important aspects of AI governance, with organizations needing to clearly articulate accountability for AI outputs and ensure that AI systems can explain their decision-making processes.