Definition
AI Risk Management is the process of identifying, assessing, and mitigating potential harms and unintended consequences associated with the development and deployment of artificial intelligence systems. It involves a systematic approach to understanding how AI might fail, behave unexpectedly, or be misused, and then implementing controls to prevent or minimize these negative outcomes. This practice ensures that AI technologies are developed and used responsibly, aligning with ethical principles and organizational objectives. It works by establishing frameworks for evaluating AI models for bias, ensuring data privacy, and maintaining system security throughout the AI lifecycle. Implementing AI Risk Management is crucial for building trust in AI systems, preventing reputational damage, and ensuring regulatory compliance. For instance, a financial institution might use AI Risk Management to ensure its loan approval AI does not discriminate against certain demographics. A common point of confusion is mistaking AI Risk Management solely for cybersecurity, when it also encompasses ethical, societal, and operational risks. This concept is increasingly relevant in domains covering AI ethics and governance, often appearing on advanced AI and cloud certification exams.