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Artificial intelligence (AI) is transforming industries, presenting both opportunities and challenges. The rise of generative AI has highlighted the need for strong governance frameworks to ensure data security and ethical implementation. Abhilash Nagilla discusses the evolution of AI governance frameworks and their significance in modern applications.
Traditional data governance models are inadequate for AI-driven applications, necessitating dedicated AI governance policies. These frameworks focus on fairness, accountability, and human oversight, aligning with emerging regulations. Effective governance enhances AI investments, operational efficiency, and stakeholder trust while mitigating risks.
• AI governance frameworks are essential for managing risks and ensuring ethical implementation.
• Continuous monitoring and adaptation are crucial for effective AI governance.
Generative AI refers to algorithms that can create new content, necessitating robust governance frameworks for ethical use.
AI governance involves structured policies to manage risks and ensure compliance in AI applications.
Data quality management is critical for improving model accuracy and reliability in AI systems.
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