AI Governance: A Guide to Building Ethical and Scalable AI Systems

AI governance strategies are more important than ever: AI adoption is at an all-time high, with a 2024 Statista report finding that 72% of companies use AI for business in at least one function. However, only a quarter of adults in the US trust AI solutions to provide accurate information. An even smaller number trust AI technology to make ethical and unbiased decisions. 

The gap between businesses’ appreciation for artificial intelligence and users’ wariness reflects the double-edged nature of generative AI. While generative AI has many benefits, such as the potential to automate work activities that take up 60 to 70% of employees' time, it has flaws. McKinsey & Co. reports that 44% of respondents’ organizations have experienced at least one consequence of irresponsible AI: typically inaccuracy, followed by cybersecurity and explainability. 

These failures add up. Over time, they damage companies’ reputations, harm consumer trust, and put businesses at legal risk. Companies can prevent these dangers by implementing ethical governance frameworks.

What is AI Governance?

AI governance frameworks establish the standards, protocols, and systems companies use to maintain responsible AI practices. “AI has the potential to revolutionize how businesses operate, but it's not always appropriate to use—or to use without human oversight,” says Ben Carle, CEO of FullStack, a leading technology and AI agency. 

Governance introduces vital human oversight, countering the potential damage of unchecked AI systems. 

However, while AI governance frameworks set the stage for responsible AI use, they are only as effective as the risks they address. Understanding these risks is crucial to implementing meaningful safeguards.

Why Are Responsible AI Practices Necessary? 

Businesses use AI to streamline operations and automate decision-making processes. However, Carle states, “AI excels at pattern detection and rule-based decision, but it struggles with ambiguity, empathy, and ethical nuance.” These systems lack human judgment, and without ethical AI governance, they may reinforce harmful patterns or produce inaccurate information that impacts users and their trust in a company. 

AI Biases in Healthcare

In 2007, healthcare providers began using the VBAC algorithm, which determined whether a patient could safely give birth without medical intervention. Unfortunately, in 2017, a study by Vyas, et al. found that this algorithm was heavily biased.

The VBAC algorithm predicted that Black and Hispanic women were less likely to have a successful natural birth after a C-section than White women. This led doctors to perform more C-sections on Black and Hispanic women, creating a cycle of Black patients having lower rates of VBAC because they were already predicted to have lower rates of VBAC.

AI Hallucinations in Legal

AI systems are also vulnerable to hallucinations. These occur when systems detect seemingly nonexistent patterns and use them to create bizarre or inaccurate outputs. A notable example happened in 2023 when a lawyer used ChatGPT to conduct legal research in a federal court case. 

The lawyer, Steven Schwartz, created a brief referencing at least six other cases, including Varghese v. China Southern Airlines and Shaboon v. Egypt Air. Upon investigation, none of these cases were real. Schwartz, his fellow lawyer Peter LoDuca, and their law firm were fined over the incident. 

There are many other cases of AI misinformation and bias causing serious harm, such as Copilot incorrectly claiming that a German journalist, a mayor, and a US radio host were all criminals. Without AI governance, these systems endanger innocent people and expose the companies behind them to significant financial, reputational, and legal harm.

Existing AI Governance Frameworks and Standards

The rise of artificial intelligence is a popular subject among experts, corporations, and governments worldwide. While many are excited about the future of this new technology, their enthusiasm doesn’t outweigh their caution, leading to the growing adoption of responsible AI practices and regulations. 

For instance, UNESCO’s Recommendation on the Ethics of Artificial Intelligence is a widely accepted AI governance baseline. Its policies apply to all 194 member states and feature 10 principles AI systems should follow. These are:

1. Proportionality and Do No Harm: AI systems must not go beyond what is necessary to achieve a legitimate aim.
2. Safety and Security: AI actors must avoid and address safety and security risks. 
3. Right to Privacy and Data Protection: Privacy must be protected and promoted throughout the AI lifecycle.
4. Multi-stakeholder and Adaptive Governance & Collaboration: International law & national sovereignty must be respected in the use of data. 
5. Responsibility and Accountability: AI systems should be auditable, traceable, and have the necessary oversight.
6. Transparency and Explainability: AI systems should maintain a level of T&E appropriate to their context.
7. Human Oversight and Determination: AI systems should not displace human responsibility and accountability. 
8. Sustainability: AI systems should be assessed on their impacts on sustainability. 
9. Awareness & Literacy: Public understanding of AI and data should be promoted.
10. Fairness and Non-Discrimination: AI actors should promote social justice, fairness, and non-discrimination.

Many non-UNESCO governments echo similar policies in their regulations, such as the US in their AI Executive Order and the EU in their AI Act. Additionally, eight global tech companies, including INNIT, Microsoft, and Salesforce, have agreed to apply UNESCO’s Recommendation when designing and deploying AI systems. 

How to Implement AI Governance Strategies in Your Organization

Building a strong AI governance framework involves a combination of clear ethical guidelines, human oversight, and ongoing monitoring. Businesses can keep their AI systems responsible through a solid code of ethics, a dedicated ethics board, comprehensive training, and specialized AI monitoring platforms.

AI Codes of Ethics

Codes of ethics are the backbone of strong AI governance frameworks. They define a company’s stance on common ethical concerns like privacy, fairness, and explainability while outlining its steps to address them. 

For example, IBM’s Principles for Trust and Transparency assure users that the company will not use their data without consent. They also outline the strategies IBM uses to protect user data, stating that their “clients’ data is their data, and their insights are their insights.” 

By publicly making these promises, IBM establishes accountability for its actions. This transparency builds trust among AI users and helps them make informed decisions about partnering with the company. A clearly defined AI code of ethics also ensures developers understand the company’s goals, creating consistent results and a responsible culture. 

When creating an AI code of ethics, companies should reference any AI regulations their local government or industry may have. Business owners should also regularly update themselves on new legislation, as laws and ethical standards may change. Companies can avoid legal trouble and reputation damage by adjusting their models to align with these policies. 

AI Ethics Boards

Ethics boards provide valuable human oversight throughout the AI development and deployment process. While a code of ethics defines the standards the company holds itself and its systems to, an ethics board ensures that responsible AI practices continue to be carried out. According to Carle, “Ethics boards are the highest level of human-in-the-loop oversight, shaping organizational policies for all AI initiatives and applications."

AI ethics boards also foster a culture of accountability and transparency within a company. They bridge the gap between developers, shareholders, and the public, explaining how the systems work and ensuring decisions about AI aren’t made in isolation. 

Responsible AI Training

An important step in establishing ethical AI governance is ensuring the entire company works towards it. For this to happen, a business must first ensure their employees understand what they are working towards. 

A survey conducted by UKG, a human resources and workforce technology company, found that 54% of employees had no idea how their company uses AI. The same study found that 75% of workers would be more excited about AI if their company were more transparent about how they were using it. 

Cal Al-Dhubaib, a globally recognized data scientist and Head of AI at Further, offers three types of AI training that companies should implement: 

1. AI Safety Training: This teaches workers how to handle sensitive data appropriately, how to use AI appropriately, and how to recognize AI-enhanced attacks.
2. AI Literacy Training: It’s helpful to show workers how harmful AI biases are, when to trust its results, and what to expect from systems. 
3. AI Readiness Training: Readiness training equips workers the necessary skills to use AI tools in their work.

Ethical AI training teaches workers about the systems they work with, how to use them ethically, and the importance of proper AI governance. In doing so, companies build a sense of transparency and trust with their teams, while empowering them to make ethical decisions in their day-to-day work. 

AI Monitoring

AI monitoring and AI governance are closely intertwined, as effective governance relies on the consistent observation and evaluation of AI systems. However, while AI ethics boards and knowledgeable employees are necessary for governance, they may miss subtle errors. AI monitoring platforms supplement human oversight, providing traceable, 24/7 supervision.

Some examples of AI governance software solutons include:

• Audit logs
• Automated monitoring
• Predictive alerts

These techniques help companies detect biases, drifts, and errors in real-time, and ensure AI systems stay compliant. AI governance tools and other forms of XAI are also useful for avoiding black boxes, as they offer insight into how a system processes data and produces outputs. 

Is AI Governance Consulting Right for Your Business? 

Navigating the complexities of ethical AI governance is no small task. From ensuring AI and machine learning compliance to implementing enterprise AI governance solutions, businesses must balance innovation and responsibility.

AI governance consulting with an AI agency offers tailored strategies to help your company align with evolving regulations, minimize risks, and build public and stakeholder trust. Whether deploying AI at scale or introducing new systems, having a dedicated partner to guide your governance efforts can make all the difference.

Ready to take the next step? Explore how expert AI consulting and cutting-edge AI governance software can elevate your organization’s ethical AI practices.