3 Key Areas of AI Governance
This is Part 2 of a series of blogs on IBM AI Governance.
As a first step to better understand where AI Governance fits in, its useful to think of common applications and use cases in these 3 categories:
AI lifecycle governance refers to processes and practices to help ensure transparent and explainable AI from model development, testing, validation, and deployment to retirement. It is wider-ranging than MLOps by providing the opportunity to practice responsible AI by design. While AI lifecycle governance is essential to risk management and regulatory compliance, today much of this is done manually and across disparate tools, creating costly overheads and an unwieldy operating model.
Risk management in this context refers to risks in the development and use of AI models and the need to identify and mitigate these risks. AI risks include operational, legal, reputational, and regulatory risks. As businesses define standards or principles for the use of AI (for example, BMW ), there are potential risks of not adhering to these. With the pace of innovation and expanding applications of AI, traditional model risk management doesn’t suffice and requires continuous monitoring from design to deployment and collaboration across an expanded range of stakeholders. Setting up frameworks are part of this effort. As an example, you can learn more here about the recently published Artificial Intelligence Risk Management Framework.
Regulatory compliance is increasingly becoming a key driver to adopt processes and practices to govern AI and how your organization uses it . This is a distinct trend in addition to several data privacy regulations that many global businesses also need to comply with. Moreover, policies and regulations are developing at different levels of government (e.g. NY hiring law at the state level or individual EU countries vs. EU) requires the ability to identify the scope and impact at more granular levels.
In addition to frameworks, processes, and skills, to effectively implement AI Governance, you need tooling that reduces overheads through automation, enables continuous monitoring of ML models through its lifecycle, and enables easier collaboration amongst a wide range of stakeholders. This is where IBM’s AI Governance solution comes in:
Moreover, these capabilities customizable to enable you to adapt easily and can integrate with your existing ML development and training stack.
You can learn more about use cases here and don’t forget to check out IBM AI Governance here, including our latest e-book.
Series: Learn more about IBM AI Governance:
Part 1: The Need for AI Governance
Part 3: Top 5 use cases for AI Governance
#IBM #AI Governance #Explainable AI #Responsible AI #Machine Learning
