A Guide to AI Governance:
Navigating Regulations, Responsibility, and Risk Management

What is AI Governance, and Why is it Important?

AI governance is a set of principles, regulations, and frameworks that guide the development, deployment, and maintenance of AI technologies. It considers various aspects such as ethics, bias & fairness, transparency, accountability, and risk management.

Its primary intent is to ensure the ethical and responsible use of AI. Its significance lies in the ability to mitigate risks associated with AI applications, including bias, privacy breaches, and unexplainable outcomes. Proper AI governance builds trust among users and stakeholders. It ensures AI technologies are used for beneficial purposes and aligned with legal and societal expectations.

Core Principles of AI Governance

At the core of AI governance, there are some fundamental principles that guide its development and implementation. These include:

• Ethical Principles
  AI governance must follow ethical principles, ensuring that AI applications respect human rights and fundamental freedoms.
• Transparency
  AI must be transparent to users and stakeholders, promoting trust in the technology’s development, deployment, and use.
• Accountability
  People who develop or deploy AI technologies should be responsible for any harm it causes.
• Fairness
  AI governance should promote fairness, preventing discrimination and bias in developing and using AI applications.
• Risk Management
  We need proper risk assessment and management to identify and mitigate potential risks associated with AI technologies.
• Auditability
  AI systems should be auditable. The processes and decisions they make should be easily traced and explained.
• Human Oversight 
  Humans should have a level of control and decision-making in AI systems to ensure ethical use.

These principles are the foundation for responsible AI governance. They are essential to consider in any framework or regulation related to AI. To understand why companies and governments invest in AI governance, let’s take a closer look at its historical development.

Historical Context and Development of AI Governance

The concept of AI Governance is not a new one. It has emerged and evolved in tandem with the advancement and spread of AI technologies. In the early days, AI governance was a relatively overlooked domain, given the experimental nature of AI. But, as AI’s potential implications and impacts became clearer, the need for structured governance became crucial.

In recent years, high-profile incidents involving AI have brought the need for governance to the forefront. For example, in a troubling episode, the Netherlands experienced a significant scandal resulting from the misuse of AI. Thousands of lives suffered severe consequences when a Dutch tax authority used an algorithm to identify suspected benefits fraud.

This scandal was known as the “toeslagenaffaire”, or the child care benefits scandal. The Dutch tax authorities used a self-learning algorithm to create risk profiles to spot fraud. However, the system was flawed. Based on the system’s risk indicators, families were penalized over suspicions of fraud.

It led to the impoverishment of tens of thousands of families, with some victims even resorting to suicide. This debacle underscores the potential devastation that can occur from automated systems without the necessary safeguards in place.

Amazon faced similar challenges with its AI recruiting tool, which was discovered to exhibit bias against women. The tool, developed in 2014, used machine learning to review resumes and rate job applicants. Amazon built it to streamline the talent acquisition process, assigning scores to candidates like Amazon shoppers rate products.

However, by 2015, the company discovered that the system was not rating candidates for technical posts in a gender-neutral way. This was because of the skewed training data, as most resumes came from men, reflecting the male dominance in the tech industry. The algorithm thus learned that male candidates were preferable, even penalizing resumes that included the word “women’s.” This eventually led to Amazon disbanding the project.

Another example is a recent settlement with the Equal Employment Opportunity Commission (EEOC) involving alleged AI bias in hiring. The EEOC v. iTutorGroup case dealt with the claim that iTutorGroup’s AI hiring tool exhibited age and gender bias. This led to the rejection of male applicants over 60 and females over 55.

The defendants denied these claims. However, the settlement highlights that AI tools causing unintended discriminatory outcomes can lead to serious legal consequences.

Incidents like these have led to a growing demand for frameworks and regulations to manage AI’s development and application. Over the years, different frameworks and models for AI governance have been proposed by various stakeholders, including policymakers, industry leaders, and academic researchers, each contributing to the maturation of AI governance.

This has eventually resulted in the recent introduction of the EU AI regulation and other relevant acts. But before diving into these regulations, let’s first understand why companies must invest in AI governance.

AI Governance Frameworks and Acts

Two primary AI governance frameworks are widely recognized today: the NIST AI Risk Management Framework and the OECD Framework for Classifying AI Systems. Both frameworks intersect in some areas, but each offers a distinctive viewpoint and application method.

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NIST AI Risk Management Framework

The NIST AI Risk Management Framework serves as an optional, industry-neutral tool designed to aid AI developers in reducing risks, seizing opportunities, and boosting the reliability of their AI systems throughout the entire development process. Designed to accommodate various sectors, its primary goal is to decrease AI-related risks and encourage responsible creation and implementation of AI systems.

This framework comprises two main components: planning/understanding and actionable guidance. In the planning and understanding section, the framework aids organizations in analyzing the risks and benefits of their AI systems, suggests ways to define trustworthy AI systems, and describes several characteristics of a reliable system.

The second part of the framework entails actionable guidance, which centers around four main elements: governing, mapping, measuring, and managing. Governing calls for a cultivated and present culture of risk management. Mapping involves acknowledging context and identifying risks, while measuring entails assessing, analyzing, and tracking these risks. Managing is integral to both mapping and measuring, highlighting the necessity of prioritizing and addressing risks based on their potential impact.

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OECD Framework for Classifying AI Systems

The OECD Framework for Classifying AI Systems provides guidance on characterizing AI tools, aiming to establish a common understanding of AI systems. The framework evaluates AI systems from five different angles:

1. People and Planet: Examines the impact of AI systems on the environment, society, and individuals.
2. Economic Context: Evaluates AI’s influence on the job market, employee productivity, and market competition.
3. Data & Input: Assesses the type of data fed into AI systems and the governing process of that data.
4. AI Model: Examines whether an AI system’s technical setup allows for explainability, robustness, and transparency.
5. Task & Function: Considers the functionality of an AI system.
   This framework aims to facilitate discussions regarding regulations and policies, steer AI developers in constructing responsible tools, and evaluate potential risks.

While both frameworks are instrumental for contemplating risk in the context of AI, they are most effectively applied to predictive AI versus generative AI. As AI continues to evolve rapidly, these frameworks will likely undergo revisions in the near future. Nevertheless, they form a crucial initial step in understanding AI risks and categorizing different AI system types.

Additionally, certain Acts have been proposed or enacted to regulate AI use and governance. Apart from the EU AI Act, which we will cover in more detail in the next chapter, some other notable regulations include:

National Artificial Intelligence Initiative Act of 2020 (NAIIA)

The National Artificial Intelligence Initiative Act of 2020 (NAIIA) is a significant regulation that proposes to advance and coordinate efforts in AI research and development. The Act aims to ensure global leadership in AI and addresses the critical areas of AI governance, including data access, privacy, bias, and accountability.

Algorithmic Justice and Online Transparency Act

The Algorithmic Justice and Online Transparency Act is another pivotal Act that seeks to promote transparency and accountability in the use of AI and algorithms. It demands that companies reveal the use of automated decision systems, including AI, and provide meaningful information about these systems’ logic, significance, and consequences.

Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence by President Biden

On October 30, 2023, the President of the United States, Joe Biden, issued an Executive Order establishing new AI safety and security standards. This Executive Order includes several directives, such as developing new standards for AI safety and security and establishing an advanced cybersecurity program.

It also addresses the protection of privacy from potential risks posed by AI and advocates for the implementation of equity and civil rights in the use of AI. Furthermore, it stands up for consumers, patients, and students by ensuring responsible use of AI in areas like healthcare and education, and it promotes innovation and competition by supporting AI research and development across the country.

Artificial Intelligence Research, Innovation, and Accountability Act of 2023 (AIRIA)

The goal of this Act is to build upon the existing efforts of the U.S. to establish a secure and innovation-friendly environment for the development and utilization of artificial intelligence, following the recent Executive Order on Safe, Secure, and Trustworthy AI and the Blueprint for an AI Bill of Rights released by the White House.

The AIRIA Act is particularly significant because it introduces new transparency and certification requirements for AI system Deployers based on two categories of AI systems: “high-impact” and “critical-impact.” It establishes a new certification regime for AI, requiring Critical-Impact Artificial Intelligence Systems to self-certify compliance with standards developed by the Department of Commerce. The AIRIA would also require transparency reports to be provided to Commerce in the housing, employment, credit, education, healthcare, and insurance sectors.

Blueprint for an AI Bill of Rights

The “Blueprint for an AI Bill of Rights” is a seminal document that addresses the significant challenges posed to democracy by using technology, data, and automated systems in ways that could potentially undermine the rights of the public. It discusses how these tools can limit opportunities and prevent access to vital resources or services while highlighting well-documented instances where systems intended for patient care, hiring, and credit decisions have proven unsafe, ineffective, or biased.

To this end, the White House Office of Science and Technology Policy has identified five guiding principles for the design, use, and deployment of automated systems in this era of artificial intelligence:

1. Safe and Effective Systems
2. Algorithmic Discrimination Protections
3. Notice and Explanation
4. Human Alternatives, Consideration, and Fallback
5. Data Privacy

These principles aim to provide protection whenever automated systems can significantly impact the public’s rights, opportunities, or access to critical needs. Each principle is accompanied by a handbook, “From Principles to Practice”, which offers detailed steps towards actualizing these principles in the technological design process.

Other Acts and Regulations

Apart from these major Acts, there are various other regulations proposed or enacted globally to govern AI usage. For instance, Acts in force in the United States include the AI Training Act, National AI Initiative Act, and AI in Government Act, alongside draft acts such as the Algorithmic Accountability Act, National AI Commission Act, Digital Platform Commission Act, and Global Technology Leadership Act.

In Canada, an anticipated AI and Data Act, part of Bill C-27, is intended to protect Canadians from high-risk systems, ensure the development of responsible AI, and position Canadian firms and values for adoption in global AI development.

European Union, apart from the EU AI Act, passed other regulations such as GDPR (General Data Protection Regulation), Digital Services Act, and Digital Markets Act, aiming to protect users’ privacy and prevent tech giants from using their market dominance for anti-competitive practices.

While the United Kingdom does not yet have a comprehensive AI regulation, the government has proposed a context-based, proportionate approach to regulation. With this perspective in mind, existing sectoral laws will be used to impose necessary guardrails on AI systems.

One such resource is the ‘Pro-innovation approach to AI regulation‘ document, which underscores the government’s commitment to fostering AI innovation. Another important resource is the ‘Algorithmic Transparency Recording Standard Hub,’ an initiative aimed at promoting transparency in AI applications.

Additionally, at the recent Bletchley Park AI summit, an array of global leaders and tech industry figures participated and expressed grave concerns regarding AI. The summit served as a critical platform for open discussions about the potential risks and rewards of AI, emphasizing the need for responsible AI governance and effective risk management strategies.

Countries like Singapore, China, UAE, Brazil, and Australia have also issued national AI strategies, laying the foundation for responsible and ethical AI governance worldwide.

ISO and IEEE Standards for AI Governance

Apart from government regulations, international standards organizations like ISO (International Organization for Standardization) and IEEE (Institute of Electrical and Electronics Engineers) have also developed standards related to AI governance.

On February 6, 2023, the International Standards Organization (‘ISO’) released the ISO/IEC 23894:2023 guide, a critical documentation aimed at AI risk management. This guidance provides essential insights to organizations involved in the development, deployment, or usage of AI systems, helping them navigate the unique risks associated with these technologies.

It serves as a roadmap to integrate risk management into AI-related operations effectively. To provide a structured approach, the guidance is divided into three core sections. The first, Clause 4, sets out the fundamental principles of risk management. The second, Clause 5, is dedicated to outlining the risk management framework, while the third, Clause 6, elaborates on the risk management processes.

Additionally, IEEE has also created a portfolio of standards to guide responsible AI governance, including the IEEE P2863™ – Recommended Practice for Organizational Governance of Artificial Intelligence.

This comprehensive guidance document sets out critical criteria for AI governance, such as safety, transparency, accountability, responsibility, and minimizing bias. It further elaborates on the steps for effective implementation, performance auditing, training, and organizational compliance.

These international standards provide a valuable reference for companies looking to establish responsible AI governance practices that align with the EU AI Act and other relevant regulations.

AI Governance Checklist for Directors and Executives

Directors and executives need to understand the implications of AI governance on their organizations and take proactive measures to ensure responsible and ethical practices. The following 12-point checklist can serve as a starting point for companies looking to develop their AI governance framework:

• Understand the company’s AI strategy and its alignment with the broader business strategy.
• Ensure AI risk owners and related roles and responsibilities are clearly defined and that those individuals have the appropriate skill sets and resources to undertake those roles properly.
• Understand the company’s AI risk profile and set or approve the tolerance for AI risks.
• Ensure AI is a periodic board agenda item, either at full board or risk committee meetings, and that the board has adequate access to AI expertise.
• Understand the legality of the use and deployment of AI, including the collection and use of training data, across the business.
• Understand how the business ensures that ethical issues involved in AI use are identified and addressed, especially bias and discrimination.
• Understand how AI systems and use cases are risk-rated (i.e., the rating criteria and assessment process), which have been prohibited, and why.
• Understand the critical and high-risk AI systems used and deployed across the business and the nature, provenance, and reliability of data used to train high-risk systems.
• Understand the trade-offs in AI decisions (e.g., accuracy vs. fairness, interpretability vs. privacy, accuracy vs. privacy, accuracy vs. adaptability).
• Ensure there are processes for management to escalate and brief the board on any AI incidents, including the organization’s response, any impacts, the status of any investigations, and learnings identified as part of the post-incident review.
• Ensure compliance with the AI risk management program is audited by the audit function in line with its third-line role.
• Ensure the AI risk owner regularly reviews the effectiveness of the AI risk management program and policies.

Key Highlights of the AI Act

The EU AI Act stands to reshape the framework for AI applications across all sectors, not confined to a specific area of law. Its risk-based approach to AI regulation ranges from outright banning AI systems with unacceptable risks to imposing various obligations on providers, users, importers, and distributors of high-risk AI systems. It also sets down broad obligations and principles for all AI applications.

Based on the final version of the Act, MEPs reached a political deal with the Council in which they established a risk-based stratification of AI, assigning obligations proportionate to potential hazards and the level of impact posed by each AI system.

Banned Applications

The EU AI Act explicitly prohibits various high-risk AI applications, identified to protect citizens’ rights and uphold democratic principles.

• Biometric Categorization Systems: Systems using sensitive traits such as political, religious, philosophical beliefs, sexual orientation, or race for categorization.
• Facial Recognition Databases: Prohibition on untargeted scraping of facial images from various sources for facial recognition databases.
• Emotion Recognition: Elimination of emotion recognition in workplaces and educational institutions.
• Social Scoring: Restriction of AI systems basing judgments on personal characteristics or behaviors.
• Behavior Manipulation: Prevention of AI systems influencing human behavior to undermine free will.
• Exploitation of Vulnerabilities: Measures against AI systems exploiting vulnerabilities based on age, disability, or socio-economic status.

Law Enforcement Safeguards

Stricter limitations and safeguards are established for using biometric identification systems by law enforcement entities. “Real-time” usage is strictly regulated and confined to targeted searches related to specific crimes or imminent threats, meticulously controlled in terms of time and location. Additionally, “post-remote” usage mandates targeted searches for individuals suspected or convicted of serious crimes, subject to judicial authorization.

Obligations for High-Risk Systems

Significant obligations are imposed on AI systems categorized as high-risk, underscoring the necessity for fundamental rights impact assessments. Citizens retain the right to file complaints concerning high-risk AI systems impacting their fundamental rights.

These systems must be transparent, explainable, and accountable through the entire process of their development, deployment, and use. Notably, specific obligations extend to AI systems influencing elections and voter behavior, ensuring transparency and accountability.

General Artificial Intelligence Systems (GPAI)

General-purpose AI systems must adhere to stringent transparency requirements, including detailed technical documentation and compliance with EU copyright laws. High-impact GPAI models are subject to even stricter obligations, encompassing assessments and mitigation strategies for systemic risks, cybersecurity adherence, and reporting on energy efficiency metrics.

Support for Innovation and SMEs

The EU AI Act encourages innovation by facilitating regulatory sandboxes and real-world testing environments. This framework aims to empower small and medium-sized enterprises (SMEs) to develop AI solutions without undue influence from larger industry players, fostering a more competitive and diverse landscape.

Sanctions and Timeline

The final text of the EU AI Act is expected to be confirmed in January 2024, followed by the crucial stages of approval through voting by the European Parliament and the Council of the European Union.

Non-compliance with the regulations outlined in the EU AI Act carries significant penalties, with fines ranging from a minimum sum or percentage of the company’s annual global turnover. The most severe violations of prohibited applications may result in fines of up to 7% or €35 million. Meanwhile, violations of obligations for system and model providers can result in fines of 3% or €15 million. Additionally, failing to provide accurate information can lead to a fine of 1.5%.

The final form of the EU AI Act will undoubtedly have a far-reaching impact on the AI landscape. Thus, organizations using or developing AI technologies must familiarize themselves with the legislation’s requirements and prepare for potential changes in their operations. It’s clear that the world is watching as the EU leads the way in comprehensive AI regulation.

What Are The Key Principles of Responsible AI?

The key principles of Responsible AI are centered around ensuring that AI systems are transparent, fair, and accountable. These principles are based on the belief that AI technologies should always serve the best interests of individuals, society, and the environment and include fairness, empathy, transparency, accountability, privacy, and safety. Let’s take a closer look at each of them:

• Fairness
  AI systems should not discriminate against any individual or group based on protected characteristics such as race, gender, or age. They should be designed and deployed to promote fairness and equality for all.
• Empathy
  Responsible AI recognizes the importance of understanding and responding to human emotions and needs. This means incorporating empathy into the design process to ensure that AI systems are used to improve people’s lives.
• Transparency
  AI systems should be transparent in their decision-making processes and clearly communicate how they work. This means making the reasoning behind decisions understandable and accessible to all stakeholders.
• Accountability
  Responsible AI requires that individuals or organizations take responsibility for AI systems’ development, deployment, and impact. This includes being accountable for potential risks and unintended consequences.
• Privacy
  AI systems should respect the privacy of individuals and handle their personal data responsibly and ethically. This means ensuring that individuals control how their data is collected, used, and shared.
• Safety
  Responsible AI aims to prevent harm to individuals or society caused by AI technologies. This includes identifying potential risks and implementing measures to mitigate them.

These principles form the foundation of responsible AI governance and should be integrated into every AI development and deployment stage. This includes data collection, algorithm design, testing, and ongoing monitoring.

What Are The Benefits of Responsible AI?

• Fairness
  AI systems should not discriminate against any individual or group based on protected characteristics such as race, gender, or age. They should be designed and deployed to promote fairness and equality for all.
• Empathy
  Responsible AI recognizes the importance of understanding and responding to human emotions and needs. This means incorporating empathy into the design process to ensure that AI systems are used to improve people’s lives.
• Transparency
  AI systems should be transparent in their decision-making processes and clearly communicate how they work. This means making the reasoning behind decisions understandable and accessible to all stakeholders.

Potential Challenges of Responsible AI Governance

While responsible AI governance has numerous benefits, it also poses several challenges for businesses. Let’s take a look at some of the frequently mentioned ones:

• The Challenge of Bias
  Human biases related to age, gender, nationality, and race can affect data collection and potentially lead to biased AI models.
  For instance, a US Department of Commerce study found that facial recognition AI often misidentifies individuals of color. This could lead to wrongful arrests if used indiscriminately in law enforcement. Further complicating matters, ensuring fairness in an AI model is challenging. There are 21 parameters to define fairness; often, meeting one parameter may mean sacrificing another.
• The Challenge of Interpretability
  Interpretability refers to our ability to understand how a machine learning model has arrived at a particular conclusion. Deep neural networks operate as “Black Boxes” with hidden layers of neurons, making their decision-making process difficult to understand. This lack of transparency can pose a problem in high-stakes fields like healthcare and financial services, where understanding AI decisions is critical. Moreover, defining interpretability in machine learning models is challenging, as it is often subjective and specific to the sector.
• The Challenge of Governance
  Governance in AI refers to the rules, policies, and procedures that oversee the development and deployment of AI systems. While strides have been made in AI governance, with organizations establishing frameworks and ethical guidelines, the rapid advancement of AI can outstrip these governance frameworks. Thus, there’s a need for a governance framework that continually assesses AI systems’ fairness, interpretability, and ethical standards.
• The Challenge of Regulation
  As AI systems become more common, the need for regulations that consider ethical and societal values grows. However, the challenge lies in creating regulation that doesn’t hinder AI innovation. Despite regulatory bodies like the GDPR, CCPA, and PIPL, AI researchers have found that most EU websites fail to meet the GDPR’s legal requirements. Furthermore, reaching a consensus on a comprehensive definition of AI that covers both traditional AI systems and the latest AI applications presents a significant challenge for legislators.

AI Risk Management Strategies

While there is no one-size-fits-all approach to AI risk management, there are several strategies that organizations can adopt to mitigate potential risks. In detail, these include:

1. Risk Identification: The first step in AI risk management is identifying potential risks. This involves thorough testing and scrutiny of AI systems during development and deployment to foresee any security, ethical, or performance-related issues that may arise.
2. Risk Evaluation: Once potential risks are identified, they must be evaluated based on their potential impact and likelihood. This enables organizations to prioritize risks and focus on those that could significantly affect their AI systems.
3. Applying Controls: After risks have been identified and evaluated, organizations need to implement controls to prevent or reduce the impact of these risks. Controls could include stricter data privacy measures, robust security protocols, or implementing ethical guidelines for AI development.
4. Regular Monitoring and Review: AI risk management is an ongoing process. Regular monitoring and reviewing AI systems is crucial to ensure that controls are effective and new risks are identified and managed promptly.
5. Adopting AI Governance Frameworks: Organizations can ensure that their risk management strategies align with industry best practices and regulatory standards by adopting recognized AI governance frameworks. This includes frameworks proposed by regulatory bodies like the EU AI Act.
6. Promoting Responsible AI: Organizations can also mitigate risks by promoting the use of responsible AI. This involves ensuring that AI systems are designed and used in a way that is ethical, transparent, and respects user privacy.

However, it is important to note we can’t look at these strategies as stand-alone solutions or isolated steps. Instead, they should be integrated into an organization’s overall AI governance framework. By taking a holistic approach to AI risk management, companies can create a robust and comprehensive system for managing risks associated with their AI systems.

How to Conduct AI Risk Assessment

A crucial aspect of risk management involves conducting a thorough AI risk assessment. This involves identifying and evaluating potential risks associated with an organization’s AI systems. Some common areas of risk that organizations should address during the assessment include bias, data privacy breaches, and algorithmic errors.

• Bias Assessment
• Privacy Review
• Error Identification
• Consequence Evaluation

The assessment should also consider the potential consequences of these risks, both for the organization and its stakeholders. This information is important for developing effective risk management strategies.

What tools and techniques can you use for AI risk assessment? This question takes us back to the importance of governance frameworks. Many of these frameworks include specific guidelines and tools for conducting risk assessments, such as AI Impact Assessment Tools or Ethical Impact Assessments. But if you’re struggling to find the right tools for your organization, consulting with experts in the field may be the way to go.

Developing AI Code of Ethics

When creating an AI code of ethics, there are several key considerations that organizations should take into account:

• Collaboration
  Involving diverse stakeholders in the development of the code can ensure that different perspectives and concerns are addressed.
• Context-Specific
  Codes must be tailored to the specific context and purpose of the AI system. For example, a code for autonomous vehicles may differ from one for healthcare AI.
• Continuous Evaluation and Updates
  As AI technology evolves, so should the code. Regular assessments and updates are necessary to ensure its effectiveness.
• Implementation
  A code is only effective if it is implemented and enforced. Organizations must have mechanisms in place to hold themselves accountable and address any ethical concerns that arise.

Implementing an AI code of conduct brings several benefits that resonate across companies, employees, and stakeholders. Firstly, it fosters ethical integrity within the organization, reflecting a commitment to responsible AI use that enhances the company’s reputation and trustworthiness.

By standardizing AI interactions across the organization, the code ensures consistency and reduces the likelihood of unethical practices and regulatory infringements. Employees, too, benefit as the clear guidelines, training, and resources empower them to use AI tools ethically and confidently.

At the same time, the AI code of conduct plays a vital role in the mentioned risk identification and mitigation, which minimizes legal repercussions and potential harm to stakeholders.

Finally, ethical AI use promotes superior decision-making, as employees can have faith in the data and insights provided by AI systems. It demonstrates a commitment to fairness, transparency, and accountability – all critical elements in building stakeholder trust.