AI Governance in 2025: An Overview

In a world where artificial intelligence (AI) seems is leaping forward and is growing at a CAGR of almost 40%, questions about governance and ethics with the use of AI are surfacing.

As humans continue to develop AI systems, it is crucial to establish proper guidelines to ensure powerful technologies like generative AI and adaptive AI are used in a responsible manner.

In this article, we'll have a look at an overview of AI governance, exploring the key concepts, challenges, and potential solutions that can shape a future with AI in a way that benefits everyone.

What is AI governance?

AI governance refers to the set of policies, laws, and regulations that govern the development, deployment, and use of artificial intelligence. It aims to address issues in AI systems such as:

• Accountability
• Transparency
• Fairness
• Privacy
• Security

The ultimate goal of AI governance is to ensure that AI is developed and used in a way that both aligns with societal values and benefits everyone. Putting AI governenace into action is still being defined, but it may include:

• Developing ethical guidelines and codes of conduct.
• Establishing regulatory frameworks.
• Promoting collaboration between different stakeholders.

(Related reading: data governance & GRC: governance, risk, and compliance.)

The importance of AI governance

As AI technologies become more advanced and integrated into our daily lives, the potential impact they can have on society is growing exponentially. Without proper governance, these powerful tools can pose significant risks to individuals, communities, and even entire nations.

For example, biased algorithms used in hiring or loan decisions can perpetuate discrimination and inequality. Automated decision-making systems in the criminal justice system could lead to biased sentencing and incarceration rates.

On the other hand, with proper governance in place, AI has the potential to bring enormous benefits, such as:

• Improved healthcare outcomes.
• Increased efficiency in industries.
• Better disaster response systems.

Therefore, it is vital to establish strong governance frameworks that can promote the responsible development and use of AI.

(Learn about Splunk’s AI philosophy & watch the on-demand webinar.)

Governments respond to AI systems

Today, several efforts are already in progress to develop effective AI governance systems. One example is the AI Bill of Rights in the U.S., which states that AI systems should be accountable, transparent, and secure. Other countries, have also developed national strategies for responsible AI development and use, including:

• Canada’s Artificial Intelligence and Data Act
• China’s Generative AI Measures

Ongoing research and discussions are also being conducted to address emerging challenges and identify potential solutions for governing AI systems.

Key concepts in AI Governance

AI governance can feel like an opaque topic: what does it really mean? Breaking it into these five concepts, however, makes it easier to grasp.

Accountability

One of the core principles of AI governance, accountability involves:

• Ensuring that individuals and organizations are responsible for the development, deployment, and outcomes of AI systems.
• Creating and reinforcing mechanisms for holding individuals and organizations accountable in case of any negative consequences or violations.

Transparency

Transparency refers to making the inner workings of AI systems accessible and understandable to those affected by their decisions. This can be achieved in many ways, including clear documentation, open-source code, and testing and validation processes.

Fairness

Fairness in AI governance means ensuring that AI systems do not discriminate against any group or individual. Ensuring fairness in AI systems involves a variety of approaches, such as:

• Auditing data for biases.
• Using appropriate sampling techniques.
• Implementing fairness metrics in model evaluation.
• Updating decision-making processes to ensure equitable outcomes.

Ongoing monitoring is also important to ensure that the system continues to behave fairly as it is exposed to new data.

Privacy

Privacy concerns play a significant role in AI governance as these technologies often involve the collection and use of sensitive personal data. It is essential to establish clear guidelines for handling this data and protecting individuals' privacy rights.

(Read up on data privacy.)

Security

Security is another crucial aspect of AI governance, especially with regard to:

• The protection of sensitive information
• The prevention of cyberattacks

Robust security measures must be put in place to safeguard AI systems and their data from malicious actors. When it comes to AI governance, there is no one-size-fits-all approach. Different countries and organizations have different priorities and concerns, resulting in variations in their governance frameworks.

To get started, you can explore AI risk frameworks such as the NIST AI risk management framework (AI RMF).

AI governance models based on human involvement

To ensure responsible AI development, we need to consider how much human oversight is required. Here are three models to consider:

Human-in-the-loop

In this model, humans are involved in the decision-making process and have the final say. Machines provide recommendations or assist in decision-making — but ultimately, a human has the power to override their suggestions.

This model allows for human intervention when necessary, ensuring that ethical considerations are taken into account before any decisions are made.

Human-on-the-loop

"Human-on-the-loop" is a concept related to the operation and oversight of autonomous systems, especially in the context of artificial intelligence (AI) and military applications.

It represents a middle ground between fully autonomous systems ("human-out-of-the-loop") and those that require continuous human control or decision-making ("human-in-the-loop"). For example, a human can intervene and abort an action taken by the AI at any time.

Human-out-of-the-loop

In this model, machines make decisions autonomously without any human involvement.

This can be useful in situations where time is of the essence or when humans may not have all the necessary information to make informed decisions. However, it also creates potential risks if something goes wrong and there is no human oversight.

Challenges in AI governance

As with any emerging technology, developing effective AI governance systems is not without its challenges. Here are some possible challenges organizations will face:

• Lack of understanding and expertise. AI is a complex field. Policymakers, regulators, and even developers may not have the necessary knowledge and skills to understand its implications fully.
• Balancing innovation with regulation. On one hand, regulators need to keep up with the rapid pace of AI advancements. On the other hand, they must ensure that proper regulations are in place to mitigate potential risks.
• Coordination between different stakeholders. AI governance involves the collaboration of various stakeholders, including policymakers, researchers, industry experts, and civil society. Coordinating these diverse groups can be challenging.
• Global and cross-border implications. AI is already a global phenomenon. So, developing effective governance frameworks that can address cross-border issues is essential.

To help address these challenges, let’s look at some potential solutions.

Potential solutions for effective AI governance

Despite the challenges, efforts are being made to develop effective AI governance systems. Here are some potential solutions that can contribute to achieving this goal:

Collaboration and coordination

Coordinating between different stakeholders is vital for effective AI governance. International collaborations, partnerships between the public and private sectors, and multi-stakeholder forums can help facilitate this. One example is the AI Governance Alliance, coordinated by the World Economic Forum.

Regulatory sandboxes

These are controlled environments where companies can test new products or services under regulatory supervision. This allows developers to innovate while regulators can:

• Monitor potential risks.
• Make necessary adjustments, both to models and to regulations.

(Know the basics about regulatory compliance.)

Ethical guidelines and codes of conduct

Many organizations have already developed ethical AI principles that can serve as a framework for responsible development and use of AI systems.

Certification schemes

Similar to product safety certifications, these schemes can provide assurance that AI systems comply with certain standards and are safe to use.

(Check out cybersecurity certifications, some of which may include AI governance components.)

Continuous monitoring and evaluation

It is essential to continuously monitor and evaluate AI systems' performance (through robust AIOps) and their impact on society. This can help identify any potential issues and inform necessary updates or adjustments to governance frameworks.

FAQs about AI governance

What roles can the public and private sectors play in AI governance?

The public sector can play a pivotal role in AI governance by setting regulations, providing oversight, and promoting transparency and accountability.

The private sector, on the other hand, can contribute by innovating responsibly, adhering to established regulations, and actively participating in conversations around ethical AI practices.

How does AI governance support privacy rights?

AI governance supports privacy rights by establishing guidelines on how sensitive personal data is collected, stored, used, and shared. This includes implementing proper data anonymization techniques, obtaining informed consent from users, and ensuring compliance with data protection laws.

Final thoughts

AI governance is crucial for ensuring the responsible development and use of AI systems. While it presents its fair share of challenges, efforts are being made to address them and develop effective governance frameworks.

As AI continues to advance and integrate into various aspects of our lives, it is essential to continuously monitor and evaluate its impact on society and make necessary adjustments to governance systems. Therefore, organizations must stay informed about current developments in the field and participate in conversations around ethical AI practices.