AI risks - Governance & Compliance
People & Blogs
Introduction
Governance in the context of artificial intelligence (AI) encompasses a broad spectrum of concerns, with managing AI risks emerging as a pivotal component. Unlike traditional data governance, which tends to focus primarily on the quality of data, AI risk governance delves into several additional layers of responsibility.
One significant aspect of AI risk governance is the acknowledgment that the risks involved extend far beyond merely handling personally identifiable information (PII). Businesses must carefully consider the various roles and responsibilities that impact their AI systems, leading to the rise of new positions such as Chief Risk Officers (CRO) or similar roles focused explicitly on AI-related risks.
When assessing AI models, it is essential to evaluate their intended use cases and continuously monitor these models once they are operationally deployed. With the emergence of stringent regulations—such as the EU AI Act—organizations must ensure compliance with these legal frameworks. Regulatory bodies are increasingly demanding accountability; when they inquire about compliance, organizations must demonstrate their adherence to the relevant regulations.
Thus, the concept of governance in AI evolution must encompass a broader set of considerations than data governance alone. This expanded framework requires organizations to actively track and report on their AI systems, ensuring that all compliance and risk factors are adequately managed and documented.
Keywords
AI risks, governance, compliance, data governance, Chief Risk Officer, EU AI Act, PII, model tracking, regulatory compliance.
FAQ
What is AI risk governance?
AI risk governance refers to the framework and practices organizations implement to manage the risks associated with artificial intelligence systems, going beyond just data quality considerations.
How does AI risk governance differ from data governance?
While data governance focuses primarily on managing data quality and safeguarding PII, AI risk governance encompasses broader aspects, including compliance with regulations and monitoring AI model performance in real time.
What role does a Chief Risk Officer play in AI governance?
A Chief Risk Officer (CRO) or similar roles are responsible for overseeing AI-related risks, ensuring compliance with regulations, and tracking the use and performance of AI models.
Why is compliance important in AI governance?
Compliance is critical in AI governance because regulatory bodies increasingly require organizations to demonstrate adherence to specific laws, such as the EU AI Act, ensuring that AI systems operate within the legal framework.
What should organizations monitor after deploying AI models?
Organizations should continually assess the operational deployment of AI models, focusing on their use cases, performance, compliance with regulations, and any associated risks.