According to a recent survey by Prosper Insights and Analytics, there’s a need for human oversight in AI (37.8%), hallucinations providing wrong information (34.2%), and transparency in data models (29.9%). With consumers and businesses worrying about AI safety, adopting an effective AI Governance framework is the need of the hour.
AI Governance sets key parameters and procedures, ensuring accurate AI deployment and mitigating risks like bias and privacy invasion. One thing is common between AI safety and AI governance: they both help humans develop beneficial AI.
However, we find a differentiator between them: AI Governance focuses on the contexts and institutions through which AI is used and built, and AI safety answers technical questions on how AI is developed.
AI Governance is organized in two different ways: the technical landscape and the ideal AI Governance. The first examines the technical constraints and inputs to the problem. Ideal AI governance looks into the ethical ways in which the laws, infrastructure, and norms are used.
As AI changes our world as we speak, one important question to ask is how to ensure it is used properly. How can we help AI tools make unbiased and fair decisions?
The answer lies in offering a balanced approach to AI governance, transparent laws, and strong ethical frameworks. By developing AI systems, we can leverage the power of AI technology to simplify work processes effectively.
The goal of AI Governance is to ensure the responsible usage of AI-based technologies. For instance, humans need to understand that AI is a machine that doesn’t work on feelings and cannot distinguish good from evil. With AI governance, we can develop a future where AI-based systems can benefit everyone.
What is AI Governance?
AI governance refers to the framework, processes, and policies designed to guide and oversee AI development and applications. It includes a range of issues like data privacy, fairness, accountability, and algorithmic transparency.
AI governance offers numerous interconnected layers, ranging from organizational structure to regulatory alignment. To help steer the deployment of AI technologies, you need to establish mechanisms like standardization in AI governance and regulatory frameworks.
Why is AI Governance Important?
AI governance is important to ensure that AI technologies align with objectives, organizational strategies, and values while fulfilling ethical principles and legal requirements. Following governance mechanisms is crucial to upholding ethical standards, managing risks, and protecting public welfare.
As humans, we can develop holistic approaches via industry standards, regulatory frameworks, and organizational policies to manage machines effectively.
Governments can establish laws and regulations, such as data protection, to set boundaries for AI deployment and development. We can also use best practices to guide responsible AI development that avoids inequality and job displacement.
Examples of AI Governance
Examples of AI governance offer a range of frameworks, policies, and practices whereby firms can implement, ensuring the responsible use of AI technologies.
Let’s look at some of the examples that demonstrate the way AI governance takes place in varied contexts.
1. The General Data Protection Regulation (GDPR)
One perfect example of AI governance is GDPR, which protects personal data and privacy. Although GDPR isn’t much focused on AI, some of its provisions are relevant to AI systems. These processes involve the personal data of individuals within the European Union.
2. The Organization for Economic Co-operation and Development (OECD)
Adopted by over 40 countries, the OECD AI principles emphasize the importance of responsible AI deployment that includes transparency, accountability, and fairness in AI systems.
3. AI Ethics Boards
Many organizations have developed ethics boards that oversee AI initiatives, ensuring alignment with societal values and ethical standards.
For example, IBM’s AI ethics board has reviewed various new AI products and services to ensure they connect with IBM’s AI principles. The company is well equipped with cross-functional teams from technical, legal, and policy backgrounds.
Levels of AI Governance
Organizations can implement numerous frameworks that develop their governance practices. A couple of widely used frameworks include the NIST AI risk management framework and the European Commission’s Ethics Guidelines for trustworthy AI.
The levels of AI governance will depend on the organization’s size, the regulatory environment, and the complexity of AI systems.
1. Informal Governance
This is an approach that is based on the values and principles of the organization. Here, there might be some informal processes like ethical review boards or internal committees. However, there is no proper framework or structure for AI governance.
2. Ad Hoc Governance
This involves the development of an intuitive AI governance framework, reflecting the organization’s values and principles that connect with relevant laws and regulations.
3. Formal Governance
This type of governance typically involves the establishment of a comprehensive AI governance framework. This reflects the organization’s values and principles. Formal governance frameworks include ethical review, risk assessment, and oversight processes.
What are the principles of the AI Governance Framework?
AI Governance is very important for ensuring faster advancements in AI tech. As generative AI has good potential across many use cases, AI-powered technologies are capable of developing new solutions like code, images, and text.
Let’s look at some of the key principles that guide companies in the development of AI technologies.
1. Empathy
Organizations need to learn the societal implications of AI. They need to address and anticipate the impact of AI on its respective stakeholders.
2. Bias control
Here, the crucial aspect is to examine training data, helping avoid embedding biases in AI algorithms. This helps ensure an unbiased and fair decision-making process.
3. Transparency
As organizations are ready to offer the reasons behind AI-driven outcomes, we need to have openness and clarity in the ways AI algorithms operate and make decisions.
4. Accountability
Companies need to set and adhere to high standards, helping manage crucial changes AI can bring. This helps in following responsible AI governance.
How are companies deploying AI Governance?
Following AI governance best practices typically involves an approach that consists of a sound system for managing and monitoring AI applications. Here is a roadmap to take into consideration
1. Visual Dashboard
With a unique visual dashboard, you can send real-time updates on the health and status of AI systems, providing an overview for assessments.
2. Automated Monitoring
Utilize automatic detection systems for drift, bias, and anomalies, helping ensure AI models are working ethically and correctly.
3. Performance Alerts
When an AI model deviates from its performance parameters, you can set up alerts, offering timely interventions.
4. Custom Metrics
We need to set up custom metrics that connect with the company’s KPIs, helping attract positive AI outcomes to business objectives.
5. Audit Trails
Maintaining accessible audit trails and logs is important for accountability, helping facilitate AI systems’ behaviors and decisions.
6. Open Source Tools
Here, we need to choose open-source tools that are compatible with different machine learning platforms, offering flexibility with community support.
By carefully working on the above-mentioned practices, firms can develop an effective AI governance framework, supporting responsible AI development. This helps ensure that AI systems are aligned and compliant with organizational goals and ethical standards.
What regulations require AI Governance?
AI regulations and AI governance practices have been adopted by numerous countries, helping prevent bias and discrimination.
1. The EU AI Act
The EU AI Act, or the Artificial Intelligence Act of the European Union, is a law that governs the use and development of AI in the European Union. This act applies different rules to AI as per the risk it poses.
The EU AI Act, the first regulatory framework for AI, restricts some of the AI uses and implements strict governance, offering transparency requirements effectively.
2. The United States’ SR-11-7
This is the US regulatory model governance used for strong and effective model governance in banking.
Under this regulation, bank officials must apply an organization-wide model risk management, which helps maintain an inventory of AI models.
As AI governance continues to improve its rules and regulations, experts feel that there is a need for responsible AI adoption – a management tool for sustainability and corporate governance.
At the Company Secretaries and Registrars Forum 2025, the panelists spoke about AI’s impact on regulatory compliance, ESG reporting, and market surveillance. Conducted by the Institute of Chartered Secretaries and Administrators of Nigeria, this forum reinforced the requirement for brands to embrace AI governance models.
Mr. Clifford Akpolo, a Data Engineering Lead, KPMG, stated in this forum that AI governance frameworks would become a norm, helping companies mitigate risks.
He further expressed that companies need to prioritize employee upskilling, AI-first policies, and cross-functional collaboration that connect with corporate strategy to drive AI sustainability. Furthermore, Mr. Clifford Akpolo encouraged companies to evaluate AI readiness and administer small AI projects for effective integration into government structures.
Following the right AI automation practices will help develop AI governance structures effectively. To help you in this regard, consider working with a specialized AI automation company. With proper AI governance procedures in place, they help you leverage quality AI systems.
