An Overview of AI Governance for 2025: Examples and Best Practices

As artificial intelligence (AI) integrates into our daily lives, organizations are searching for ways to ensure the technology is used in a way that respects societal values and human rights. AI governance has emerged as a means of managing the technology’s risks and impact on society. 

What is AI Governance?

AI governance refers to the set of frameworks, policies, and guardrails used to ensure that AI technology is used in a productive, ethical manner. The purpose is to minimize risk and promote the ethical development and adoption of AI technologies. As AI usage increases, society needs a way to close the gap between accountability and ethics in the advancement of these technologies. 

By implementing best practices and policies within AI governance, organizations can reduce the risks associated with the use of the technology, such as bias, privacy infringement, and discrimination. 

It’s worth noting that the nature of AI development makes it susceptible to human error and bias. With governance, organizations have a structured approach to mitigate such risks and encourage responsible, ethical development. This is done through AI policy, regulation, data governance, and the use of well-trained data sets. Such oversight is essential to align AI practices with ethics and societal values.

Why is AI Governance Needed?

So, why is AI governance needed? Imagining a world without AI governance paints a clear picture. Without governance and frameworks to manage the development and usage of AI, people could experience unethical, immoral, and discriminatory practices from the technologies. Organizations would face legal and financial consequences as a result. On a larger scale, society could experience an increase in incidents of injustice and violations of human rights. 

Let’s take a closer look at some key reasons why AI governance is needed:

Compliance

AI governance can ensure that the development of the technology complies with current laws and regulations such as those related to data security and privacy. 

Ethics

As mentioned earlier, there are societal repercussions to consider when it comes to the development and usage of AI technology. AI governance helps mitigate these risks and increases accountability.

Trust

Improper use of AI has already eroded trust in the technology. AI governance can help organizations improve trust in the technology for employees, customers, and other stakeholders. Users and those affected by the technology can gain a better understanding of the systems, their inputs, and their outputs.

Why is AI Governance Important?

AI governance is important for society and organizations alike. Overall, it is key to ensuring compliance, transparency, trust, and ethical use of the technology. As AI becomes more deeply integrated into society, AI governance can help prevent misuse, reputational damage, and ethical harm. One of its key benefits is providing oversight. Governance offers the necessary frameworks required to ensure technological innovations do not compromise the safety of society or impinge on human rights.

Beyond oversight, AI governance also allows for creating and maintaining ethical standards. Compliance is important but social responsibility matters even more. Having principles, guidelines, and policies in place ensures that AI is designed with responsible use in mind from the start. This becomes the essential foundation for AI development and the guiding set of principles for its use. 

Furthermore, AI governance increases transparency. As AI technology is used to make decisions, it is important to hold these systems and those who employ them accountable for making ethical and responsible decisions.

Principles in AI Governance

Principles in AI governance can guide the ethical development and usage of AI technology. No universal standard exists for sourcing AI governance principles, but many organizations rely on the NIST AI Risk Management Framework, the OECD Principles on Artificial Intelligence, and the European Commission’s Ethics Guidelines for Trustworthy AI. 

These frameworks include the following principles:

Accountability

Organizations should embed ethics and morals into the development and usage of AI, taking responsibility for its effects. 

Transparency

Organizations must be clear and honest about how the technology is developed, used, and particularly, how it is leveraged to make decisions. 

Fairness

Training data must be thoroughly inspected to avoid introducing biases into AI algorithms. Decisions should be reviewed by people for bias control. 

Privacy

Organizations should adhere to data security principles and ensure that data is not misused or accessed without consent. 

Security

Likewise, organizations must implement robust cybersecurity measures, such as encryption, access controls, and threat detection mechanisms.

Empathy

Organizations should develop and use AI technology with empathy, anticipating its potential impact on individuals and society as a whole. 

AI Governance Best Practices

There are several different forms of AI governance to consider. Before delving into the best practices, it’s helpful to keep in mind the three core approaches most commonly used by organizations:

Informal governance

This first level of AI governance is centered around organizational principles and building processes to align those principles with AI development and usage. However, it lacks a structured framework to follow.

Ad hoc governance

This next level of governance has more policies and procedures in place to address concerns and challenges associated with AI usage and development. 

Formal governance

Formal AI governance is the most structured approach with a rigorous framework and related policies and processes. It adeptly aligns organizational values, compliance requirements, and other standards with AI usage and development. 

Following best practices for AI governance can help organizations move from informal governance to formal governance, increase trust in the technology, and mitigate risks by employing:  

Collaboration and Coordination

The most effective AI governance approaches involve organizational stakeholders across departments and subject matter expertise. Bringing together these stakeholders to collaborate on ideas and coordinate efforts serves as a force multiplier and allows for the transparent exchange of ideas as well as incorporating diverse perspectives. This best practice is key to ensuring that AI governance frameworks address a range of challenges and concerns. 

Transparent Communication

Regular, open communication is required to build and maintain trust in AI. All stakeholders, including end users, employees, and the community, should receive communication about how AI is used, developed, and the data sets it relies on. This practice is particularly important if the technology is used for automated decision-making. Consider communication channels, campaigns, and frequency. 

Regulatory Sandboxes

Regulatory sandboxes allow organizations to test AI technology in a controlled environment. Organizations can test drive applications while complying with regulatory requirements. They can also address challenges and risk factors as they emerge in a way that mitigates risks. 

Ethical Guidelines and Codes of Conduct

Organizations must develop ethical guidelines and codes of conduct for AI development and usage. Consider organizational values as well as societal values such as transparency, accountability, and respect. These guidelines and codes of conduct can serve as a guiding framework for all actions related to AI. 

Continuous Monitoring and Evaluation

Over time AI applications can degrade and veer from their design. For this reason, continuous monitoring and evaluation are essential. It is crucial to evaluate data sets, potential human errors, and the introduction of bias rigorously. By continuously monitoring and testing these applications, you can ensure they stay on track and serve their intended purpose. 

To bring these best practices to life, it’s important to create visual representations of your AI governance framework. For example, you could develop a dashboard that represents the current state of your AI applications, its health score, and related performance alerts for when a model deviates from its purpose. In many cases, incorporating automated detection systems is useful for quickly responding to bias, drift, or performance issues. 

Examples of AI Governance Challenges

As organizations work toward AI governance, they will encounter some common challenges that may threaten the effectiveness and efficiency of their frameworks. These challenges continue to grow as the technology integrates into all aspects of our lives. By being aware of these common challenges, organizations can prepare to address them proactively. Some of the common challenges for AI use and development include:

Complex technology to understand

AI has a level of opaqueness that makes it difficult to understand the technology and often intimidating for those who do not interact with the technology regularly. When people do not have sufficient knowledge of the technology and how it is used, they may become more distrustful. Furthermore, this lack of understanding makes it more challenging to predict its potential implications. 

Lack of accountability

It can often be difficult to determine who “owns” the effects of AI applications. This lack of accountability can allow negative effects of AI to persist without being addressed. Codes of conduct, ethical guidelines, and regulations can help address this challenge. 

Bias

Bias is easily introduced in AI data sets, and the algorithms themselves can be biased as well. This is particularly dangerous when the technology disperses misinformation and disinformation. Bias can also affect decisions made by AI technology, leading to unfairness and injustice in society. 

Constraints on innovation

Developers of AI may feel hindered by regulations. The breakneck pace of innovation can be slowed down by requirements and compliance standards. However, regulation is necessary to ensure AI functions appropriately in society. 

Constant evolution

It is challenging to keep up with the evolution of AI technology, and its effects can be unpredictable. This makes it difficult to develop policies and procedures quickly enough to address them proactively. For this reason, it may appear that many AI policies and frameworks lag behind the technology and its function. 

Collaboration constraints

Governing AI effectively requires working among multiple stakeholders, institutions, and countries. Coordinating efforts across these groups can be a logistical challenge.

Regulations and History of AI Governance

As AI continues to advance and grow in adoption, governmental agencies are working to create regulations for its development and usage. These regulatory models and directives are some of the most critical in the history of AI governance thus far. 

US Federal Reserve SR-11-7

Supervision and Regulation Letter SR-11-7 is a regulatory governance standard set forth by the United States Federal Reserve Bank to provide guidance on risk model management. According to SR-11-7, bank officials are required to apply company-wide model risk management initiatives. They must also keep an inventory detailing which models are currently in use, under development, or recently retired. Furthermore, they must demonstrate that these models serve their intended business purpose, are up to date, and have not drifted from their original purpose. Finally, individuals unfamiliar with the model must be able to comprehend its operations, limitations, and key assumptions. 

Canadian Directive on Automated Decision-Making

Canada’s Directive on Automated Decision-Making is a policy instrument that guides the Canadian government in its use of AI for automated decision-making. This directive defines automated decision-making as fully or partially using algorithms and computer code to make decisions without human intervention. The directive’s purpose is to ensure that the use of these systems is transparent, accountable, fair, and respectful of privacy as well as human rights. 

AI regulations in Europe

AI package 3, presented by the European Commission, centers on building trust in AI technology and includes a proposal for a legal framework on AI. It proposes that any AI systems deemed high risk must follow strict requirements, and it bans those deemed to pose an unacceptable risk. The European Commission has also developed guidelines for trustworthy AI development and usage, focused on fairness, accountability, transparency, and the need for human oversight. Programs such as Horizon Europe are used to drive the development of innovative AI technology that aligns with European values and standards. 

AI governance in the Asia-Pacific region

Several countries in the Asia-Pacific region have developed frameworks for AI governance. One example is the Model AI Governance Framework in Singapore, which focuses on key areas of accountability, transparency, fairness, and security to ensure the responsible and ethical use of AI. Japan has also established guidelines around similar principles for AI research and development. Meanwhile, Australia has created an AI Ethics framework designed to guide the responsible development and use of AI technologies, emphasizing accountability, transparency, fairness, and people-centered values.

Just as AI technology continues to evolve, so must the frameworks used to govern it. Despite the challenges, investing in the development of ethics guidelines and principles is worthwhile to ensure the technology is used effectively, ethically, and responsibly. 

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