Executive summary:
An AI governance framework (AIGF) provides organizations with a structured approach to AI implementation. It helps manage the ethical, legal, and operational aspects of AI systems. As AI becomes increasingly integrated into business operations, an AIGF ensures that your AI initiatives remain transparent, accountable, and aligned with both your business objectives and societal values.
Reasons for implementing an AIGF include:
By implementing a robust AIGF, your organization can harness the transformative potential of AI while mitigating associated risks. This proactive approach not only protects your business but also builds trust with customers, employees, and regulators in your AI-driven initiatives.
Don't navigate the complexities of AI governance alone. Schedule a free consultation with Talbot West to discuss how we can help you develop and implement an AI governance framework tailored to your organization's unique needs and challenges.
An AI governance framework promotes transparency, accountability, and overall alignment for AI initiatives. It helps steer the deployment and integration of AI technologies into the enterprise.
AI governance is the umbrella discipline under which an AIGF sits. It’s the overall process of managing and overseeing the ethical, legal, and operational aspects of AI systems. It includes setting policies, ensuring compliance, and aligning ethical considerations with business goals. It also includes the ongoing monitoring of AI activities to ensure responsible and effective AI use within an organization.
An AI governance framework is the structured set of guidelines, policies, and procedures that form the foundation of AI governance. It provides a detailed blueprint for implementing and maintaining AI governance, including specific roles, responsibilities, and processes to manage AI systems ethically and effectively.
AI implementation is fraught with risks, and at the same time brings transformational potential to your organization. An AI governance framework addresses the risks of AI, while leaving you to enjoy the rewards. Here are the main reasons you need to invest in an AIGF.
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If you want to take things a step further, talk to us about how we can accelerate your AI governance program and shorten your learning curve. We provide no-nonsense advice and guidance on governance issues.
The NIST AI Risk Management Framework is a comprehensive guidebook for organizations using or developing AI. Think of it as a set of best practices and recommendations to help ensure AI systems are used safely, ethically, and effectively.
While we recommend you develop your own AIGF, the NIST document can be a useful starting point.
At its core, the NIST framework aims to:
The framework is divided into four main parts, which you can think of as steps that you’d follow in establishing your own corporate governance protocols:
The NIST framework is designed to be flexible, recognizing that AI is used in many different ways across industries. It can be adapted to fit the needs of different organizations, whether they're tech companies, healthcare providers, or government agencies.
This framework addresses risks that are unique to AI. For example, it considers issues such as bias in AI decision-making, the explainability of AI systems, and the complex interactions between humans and AI.
The AIGA (Artificial Intelligence Governance and Auditing) AI Governance Framework was produced by researchers at the University of Turku in Finland to bridge the gap between AI ethics principles and real-world implementation. It sets forth a set of AI governance standards and ethical principles that serve as a complement to the NIST framework.
The AIGA framework is structured like a three-layer cake:
These layers interact with each other, which is why the framework is sometimes called the "Hourglass Model." It shows how decisions and influences flow both up and down through these layers.
The AIGA framework has the following goals:
The framework emphasizes the importance of involving experts, from AI developers to legal teams to end-users, in the process of governing AI.
The AIGA framework isn't just about following rules. It's about creating a culture of responsible AI development throughout an organization. It provides concrete tasks and recommendations, aligning with current and upcoming regulations like the EU AI Act.
If you’re in the beginning stages of AIGF implementation, you’d do well to familiarize yourself with both the AIGA and the NIST frameworks.
Here are some real-world examples of AI governance frameworks in action, from major corporations who are embracing AI governance and staying ahead of the game.
Deloitte's Trustworthy AI framework is a comprehensive approach to help organizations develop and use AI systems responsibly. The main idea is that trustworthy AI doesn't just happen by accident; it needs careful planning and management. Deloitte's framework aims to align people, processes, and technologies to create AI systems that are reliable, ethical, and beneficial.
From our perspective, Deloitte’s framework is a valuable addition to the corpus of quality AI governance frameworks that include those of AIGA and NIST.
Microsoft's Responsible AI Standard is an AI governance framework for the development and deployment of AI systems. Think of this standard as Microsoft's playbook for creating AI that's trustworthy and beneficial to society. It's based on six main principles:
Microsoft's standard is noteworthy for its comprehensive coverage of accountability, fairness, and human oversight. It’s a great addition to the other frameworks we’ve covered here.
We’re here to help you with any aspect of AI governance. Schedule a free consultation to learn how we can solve your problems. Or, check out our service offerings for the full scope of our offerings.
An AI governance framework template provides a basic starting point framework that organizations can customize to fit their specific needs, industry context, and AI applications. Here at Talbot West, we have several AI governance framework templates for different use cases, which we modify as needed to suit our clients and their needs.
Here are the most important metrics to track in your AI governance initiative:
AI governance happens across multiple levels of society, touching many regulatory frameworks and governance structures. Here are just a few of the levels of AI governance that co-exist:
There are several competing AI governance tools on the market. Don’t fall for “shiny object syndrome” and think that a tool will solve your governance issues. You still need to think through the issues carefully and create your own framework. After doing so, it’s possible that one of the following tools could be a valuable addition to your governance protocol.
None of these tools offers a “magic bullet” for AI governance. If you’d like Talbot West’s help, we provide in-depth tool assessment and recommendations, which shortcut a lot of your testing and implementation timeline.
Talbot West bridges the gap between AI developers and the average executive who's swamped by the rapidity of change. You don't need to be up to speed with RAG, know how to write an AI corporate governance framework, or be able to explain transformer architecture. That's what Talbot West is for.
