AI Governance - All you need to know
Learn the essentials of ethical AI governance to build secure and transparent AI systems.
What is AI Governance?
AI governance refers to the safe, responsible and legal use of AI during research, development and deployment. It is the framework of policies, procedures and ethical considerations used to oversee the development, deployment and maintenance of AI systems.
AI governance ensures these systems operate within legal and ethical boundaries, align with organisational values, and mitigate risks such as bias and privacy breaches.
Why is AI Governance Important?
AI governance is crucial for achieving compliance, trust, and efficiency in AI development and application. It prevents harm and misuse, manages risks like bias and privacy issues, and improves operational efficiency. Effective governance fosters trust and acceptance, ensuring AI systems are reliable and trustworthy, which is essential for widespread adoption and responsible innovation.
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Levels of AI Governance
The levels of governance can vary depending on the organisation's size, the complexity of the AI systems in use, and the regulatory environment in which the organisation operates. Below is an overview of these approaches:
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Informal
The least intensive approach, based on the organisation's values and principles. It may involve informal processes like ethical review boards or internal committees but lacks a formal structure or framework. This level has limited or no formal structure, with some informal practices in place.
Ad-hoc
A step up from informal governance, it involves several policies and processes in place for AI oversight, implemented in response to a particular threat or incident. Generally, these are one-off initiatives not part of a wider strategy.
Formal
The highest level of governance, involving a comprehensive AI governance framework. This level includes a complete list of policies and processes covering the organisation's entire use of AI, reflecting the organisation's values and principles (ethics), and aligning with laws and regulations.
Existing AI Governance Frameworks
ISO 42001
An international standard focusing on the requirements to build an AI Management System (AIMS). It addresses ethical, security, and transparency considerations for responsible AI development and use.
AI Bill of Rights
The AI Bill of Rights includes five principles for AI design and use: ensuring safe systems, preventing algorithmic discrimination, protecting data privacy, providing notice and explanation, and offering human alternatives and fallback options.
OECD AI Principles
The OECD AI Principles, adopted by over 40 countries, emphasize responsible AI stewardship, focusing on transparency, fairness, and accountability. Policymakers, including the EU and UN, use these principles in their legislation.
Components of Governance
Ethics, Regulation, and Transparency are the core pillars of why we need governance in the first place. These pillars ensure AI systems are developed, deployed, and maintained responsibly, aligning with organisational values, legal requirements, and society's ethical standards.
Ethics
A set of ethical principles recognises the accountability that an organisation has for any repercussions of its AI implementations. These principles ensure AI implementation through:
Moral Foundation- Core values
- Organisational mentality
Regulation
Regulation involves identifying and adhering to relevant laws and guidelines to manage the risks associated with AI systems. This is based on several factors to ensure compliance and lawful operations:
- Location
- End users
- Industry
Transparency
Transparency ensures that AI processes and decisions are understandable to all stakeholders. By fostering clarity and trust, transparency in AI governance addresses key principles such as:
- Explainability
- Accountability
- Communication
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Examples of Key Regulatory Frameworks
EU AI Act Effective from August 2026, focusing on risk-based compliance.
US AI Legislation Various state and federal AI bills and existing regulations.
Interim Measures for the Management of Generative Artificial Intelligence Services (China).
Artificial Intelligence and Data Act (Canada).
AI Act (South Korea).
Accountability and Risk Management
Accountability and responsibility are central to effective AI governance. While responsibility refers to tasks and duties, accountability means being answerable for the outcomes.
Risk Management involves identifying, analysing, assessing, and mitigating potential risks. The process, adapted from quality assurance frameworks like ISO 9001, includes:
Secure by Design
Quality and Security Certifications
Building solutions is serious business in the modern day. That's why we're proud to be certified by the British Standards Institute (BSI) in both Information Security (ISO 27001) and Quality Management (ISO 9001).
Achieving ISO 9001 underscores our commitment to delivering high-quality AI solutions with consistent excellence across all projects. ISO 27001 certification demonstrates our dedication to maintaining the highest standards of information security, ensuring the protection of client data.
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