Cloud Security Alliance
Sinopse: The widespread adoption of sophisticated machine learning (ML) models presents exciting opportunities in fields like predictive maintenance, fraud detection, personalized medicine, autonomous vehicles, and smart supply chain management. While these models hold the potential to unlock significant innovation and drive efficiency, their increasing use also introduces inherent risks, specifically those stemming from the models themselves.
Unmitigated model risks can lead to substantial financial losses, regulatory issues, and reputational harm. To address these concerns, we need a proactive approach to risk management. Model Risk Management (MRM) is a key factor for fostering a culture of responsibility and trust in developing, deploying, and using artificial intelligence (AI) and ML models, enabling organizations to harness their full potential while minimizing risks.
This paper explores the importance of MRM in ensuring the responsible development, deployment, and use of AI models. It caters to a broad audience with a shared interest in this topic, including practitioners directly involved in AI development and business and compliance leaders focusing on AI governance.
