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The global focus on training AI models and drafting related laws is insufficient, according to Dr. Rumman Chowdhury, the US science envoy for AI. She emphasizes the critical need for rigorous testing of AI systems to ensure their security and safety before deployment. Additionally, the enforceability of existing laws remains a significant challenge, particularly in the tech sector, where accountability mechanisms are often lacking.
Dr. Chowdhury highlights the importance of addressing bias and discrimination in AI, particularly as many models are trained on data that may not be representative of diverse populations. She points out that solutions for agricultural improvements, for instance, must consider local contexts to avoid biased outputs. Furthermore, building a robust AI talent pool through collaboration among public, private, and academic sectors is essential for the future of AI development.
• Testing AI for security and safety is currently lacking.
• Bias in AI models can lead to inappropriate outputs for diverse regions.
AI security involves ensuring that artificial intelligence systems are safe from vulnerabilities and threats.
Bias in AI refers to the tendency of AI models to produce unfair or prejudiced outcomes based on skewed training data.
AI testing is the process of evaluating AI systems to ensure they function correctly and safely before deployment.
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