AI governance is the developing framework regarding artificial intelligence (AI) technologies, use, and deployment. This framework consists of policies, regulations, standards, and ethical guidelines that are intended to manage, control, and direct the continued development and deployment of AI.
AI governance is being talked about at very high levels and with the leaders of industry within the fortune 500 and more. AI is here to stay. At the time of writing, we are at the advent of a truly revolutionary era of technology disruption with AI leading the way.
We have curated as much as possible on the subject of AI governance and will breakdown as much as possible. This was accurate at the time of writing, however, AI is a fast evolving topic. Be sure to keep up with the changes that affect your exact industry.
The author of this post was involved in the development of GPS and cellular communications back when those technologies were initially being introduced to the civilian sector. While today, there are many bad actors abusing these technologies, the process of initially developing disruptive technologies are heavily scrutinized and monitored.
The goal back then was for the greater good and the advancement of the technology to benefit civilization. We believe that is the same goal regarding AI.
AI Governance Components
We will basically highlight each component of AI Governance, followed by delving into basic explanations of each component.
AI Regulation and Legislation
These are items that most of us are already familiar with in some form. They have further expanded into AI governance. There are developing laws and regulations (at national and international levels) that address issues with AI such as data protection, privacy, and AI ethics. A couple examples include the European Unions’ (EU) General Data Protection Regulation (GDPR) and the Artificial Intelligence Act.
AI Ethical Guidelines
Principles for ethical AI development, such as transparency, fairness, accountability, and respect for human rights. Professional Organizations such as the Institute of Electrical and Electronics Engineers (IEEE) have developed ethical standards for AI. The IEEE is a professional organization that supports the advancement of engineering, computer science, and information technology.
AI Standards and Best Practices
Technical standards and best practices ensure AI systems are safe, reliable, and interoperable. Organizations such as ISO and NIST work on these standards. The International Organization for Standardization (ISO) is an independent, non-governmental international organization. The ISO brings global experts together to agree on the best ways of doing things, followed by publishing international standards.
AI Risk Management
Every entity involved at this stage is aware of he potential benefits of AI. Everyone also understands that the risks of AI are formidable, for society as well as for companies. There are developing strategies to identify, assess, and mitigate risks associated with AI, including unintended consequences, bias, and security threats.
AI Accountability and Oversight
Developing desired societal and ethical outcomes regarding AI includes developing mechanisms that ultimately hold developers, as well as users of AI accountable. Artificial Intelligence accountability includes audits, certifications, and multiple oversight bodies.
AI Public Engagement and Transparency
Standards that evolve from AI governance to enhance transparency, quality, and reliability while also mitigating risks and maximizing rewards includes involving major stakeholders in the decision-making process. The public needs to understand how AI systems work, and the promotion of public education on AI is part of the ongoing development process.
International Cooperation and AI
Just like back in the early days of the development of GPS, global collaboration is key. World leaders in the AI space need to discuss issues such as the flow of data, cyber security, and the ethical use of AI across different jurisdictions.
Please understand that this article is discussing AI on a level far beyond generating funny cat videos and images. AI has the ability to synthesize, analyze and act on enormous amounts of data in nanoseconds.
AI is an extremely powerful technology that needs to be responsibly developed. If done right, AI may truly change the world for the better.
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Challenges in AI Governance
AI is in a rapid state of technological evolution. AI is evolving at a rate faster than any other revolutionary and disrupting technology of a similar scale in the past. AI is evolving so quickly that it is often outpacing regulatory frameworks.
There are many global differences that can complicate and even slow down AI regulatory frameworks. The many cultural, legal, and ethical differences can complicate global AI governance. One way to attempt to simplify this is to balance innovation with regulation.
Simply put, there has to be a balance to ensure that regulations do not hinder AI innovation while protecting societal interests. There will be uncomfortable issues to address such as bias and fairness. We will also need to address how AI systems could lead to discriminatory practices.
Examples of Global AI Governance in Action
As previously mentioned, the European Union (EU) AI Act is a comprehensive regulatory framework for the use and development of AI within the European Union. The AI Act categorizes AI systems by risk level and aims to impose strict requirements for high-risk AI applications.
The AI for Good Global Summit is hosted annually in Geneva, Switzerland. It is the leading United Nations platform for global and inclusive dialogue on Artificial Intelligence.
AI governance is ever evolving. At the time of writing, the goal is to harness the potential of AI while safeguarding against its risks. The current evolution of AI governance includes technologists, ethicists, lawmakers, and society at large in the attempt to create a balanced and beneficial AI ecosystem.
AI Governance Components – Focused Look
AI Regulation and Legislation
The regulation and legislation of AI developments are legal frameworks established by governments intended to guide, control, restrict the development, deployment, and use of AI technologies. The goal is to address various AI concerns such as privacy, ethics, security, and societal impact.
AI Privacy and Data Protection
GDPR (General Data Protection Regulation): Although this is not exclusively about AI, this European Union regulation impacts AI because it already governs how data (which is crucial for AI inputs) should be handled.
CCPA (California Consumer Privacy Act): This is similar to the EU GDPR but is specific to the State of California. It focuses on consumer rights regarding personal data.
EU AI Act: The European Union Artificial Intelligence Act is possibly one the most advanced pieces of AI-specific legislation. The EU AI Act classifies AI systems according to the risk they pose. It then imposes obligations accordingly. It factors for high-risk AI systems and imposes strict AI requirements such as risk assessments, transparency, and human oversight.
The United States Algorithmic Accountability Act. This Bill was introduced in 2023 to direct the Federal Trade Commission to require impact assessments of automated decision systems and augmented critical decision processes, and for other purposes.
It’s basically to ensure companies using AI (to make significant decisions) are transparent and accountable.
Ethical and Bias Mitigation in AI
There are developing AI laws and amendments that address ethical concerns such as bias in AI algorithms. This is important as AI only does what it’s told so to speak. Hence, the Large Language Models (LLM’s) need to be “trained” without bias.
The EU AI Act mandates that high-risk AI systems must be trained with data sets that avoid bias and discrimination. This is one item of critical importance to keep an eye on as AI develops.
AI Security and Safety
This goal of this type of AI legislation is to ensure that AI systems are safe for human interaction and do not pose significant risks. In 2024, we saw a lot of developments regarding autonomous vehicles, drones, and other AI-driven technologies. This type of AI legislation attempts to demonstrate that safety is paramount.
AI Intellectual Property (IP) Rights
This section of AI regulation attempts to address who owns AI generated content or inventions. It’s a very complex topic with the goal of determining how AI can be used without infringing on existing IP rights.
Cross-Border Data Flows in AI
There is a invalidated previous approach on global AI regulations such as the EU-U.S. Privacy Shield (invalidated in 2020). It was a legal framework between the U.S. Department of Commerce and European Commission regarding the transfer of personal data between the European Union (EU) and the United States. This too will be a hot topic as AI data privacy and sharing needs to be regulated.
AI Governance Challenges
Global Harmonization: There are currently 195 recognized countries in the world and they all have different AI regulatory approaches. One can imagine how complicated international AI development and deployment can get.
Keeping Pace with Technology: AI is evolving at an extremely rapid pace. Politicians and legislation can fall behind in the movement, progress, or development of AI, thereby creating regulations that are outdated as soon as they are enacted.
Incentivizing Innovation vs. Regulation: We are all familiar with this in some form or fashion. Eliminating corruption and creating a balance where AI regulation does not hinder innovation, makes it fair to even the smallest of AI entity, and ensures safety, ethical use and transparency.
Global AI Governance Examples in Practice
Canada’s Bill C-27: This is a Consumer Privacy Protection Act that includes language regarding artificial intelligence and data (Data Act – AIDA). It sets out to regulate AI systems that pose high risks to health, safety, or human rights.
China’s AI Regulations: China has implemented several AI regulations in attempt to control the use of artificial intelligence. The goal is protect the public interest and national security, safeguard the rights of individuals and organizations, promote the healthy development of the AI industry, ensure ethical considerations are met, and protect data privacy. There have been some administrative AI provisions regarding “Deep Synthesis of Internet-based Information Services” in attempt to regulate deep synthesis content (synthetically generated images and videos).
AI Governance and Legislation Summary
AI regulation and legislation is a complicated task. If we are to properly advance the development of AI, regulation and legislation is crucial in shaping how it is integrated into society. We need to ensure that AI is used in ways that respect human rights, privacy, and societal norms while also fostering AI innovation and development.
AI will in fact continue to evolve. There is almost no stopping it at this point. The makers of the legal frameworks surrounding AI need to be efficient and steadfast. The AI policies should require ongoing global dialogue between all parties involved in the development, as well transparency with the public.
AI Governance and Ethical Guidelines
AI governance and ethical guidelines are the core sets of principles and standards created to ensure that AI technologies are developed and deployed in ways that are morally sound, fair, and beneficial to a global society. The following AI guidelines are being considered at very high levels:
Transparency. AI technology and systems should be transparent. Not only in how they work, but in how they make decisions, as well as the journey they take in the decision-making process. Such decisions need to be able to be explained and understood by all AI users.
Fairness and Non-discrimination. AI technology should be natively designed to avoid bias and discrimination. This requires Large Language Model (LLM) training utilizing diverse data sets. LLM’s should be regularly audited for truth and the elimination of bias to ensure equitable outcomes across different groups.
Accountability. This should be obvious objective for all involved. AI developers and everyone involved in the deployment of AI technology should function responsibly. Multiple measures should be in place to hold those accountable for negative outcomes, including any harm caused by AI.
Privacy and Data Protection. Ethical developments in AI must absolutely respect user privacy. In addition, all data involved with AI should be protected with the highest standards. This will ensure that data is used and stored securely and only with proper consent.
Safety and Security. Artificial Intelligence must be safe in its operation. Multiple measures should be put in place to prevent misuse or unintended consequences that could lead to harm.
Human-Centric. AI should intend to augment human capabilities, not replace or undermine human rights, autonomy, or dignity. AI technologies should serve human interests and well-being. AI advancements should aim to preserve this human-centric approach.
Sustainable. Not only should the environmental impact of AI be considered (energy consumption and more), but AI hardware systems should be forward thinking in terms of the product lifecycle.
Current Ethical Frameworks and AI Initiatives
The Institute of Electrical and Electronics Engineers (IEEE) Ethically Aligned Design (EAD) provides guidelines for ethical considerations in autonomous and intelligent systems.
The Organization for Economic Cooperation and Development (OECD) is an International Organization that promotes economic growth and sustainable development. They have developed the OECD AI Principles that promote the use of AI in ways that are innovative and trustworthy. In addition, their principles request that AI developments respect human rights and democratic values.
Montreal Declaration for a Responsible Development of AI. This is an initiative of the Université de Montréal regarding the responsible development of AI. The Montreal Declaration for a Responsible Development of AI proposes values such as well-being, autonomy, justice, privacy, and knowledge to guide AI development.
AI4People: The AI4People Institute was involved at the infancy stages of the AI regulatory process that ultimately led to the AI Act in Europe. This is significant as it is the world’s first AI regulation. At the core, the focus is on human-centric AI, emphasizing human dignity as the foundational value.
AI Governance Ethics Challenges
The process of implementing ethics on a operational level where broad ethical principles are actually turned into specific and actionable guidelines for AI developers and businesses has been met with many challenges. They have been riddled with various challenges due to the global nature of the impact of AI.
Cultural and Contextual Differences: Standards in ethics can vary significantly across the many global cultures, thereby reaching a global consensus is challenging to say the least.
Balancing Ethics with Innovation: Ensuring that ethical practices, rooting out political corruption and more, do not hinder honest and fair AI technological advancement and innovation.
Enforcement and Compliance: There are many self regulated industries already in existence. There are just as many unregulated industries too. We can learn all types regarding the monitoring of ethical guidelines to ensure that standards are followed.
Practical Application: Simply put, design AI with ethics in mind. Ensure that organizations are fostering ethical considerations and culture from the onset of the AI development process.
Ethics Boards or Advisory Committees: Proven trustworthy companies can set up these boards and committees to oversee AI development from an ethical standpoint.
Public Engagement: Remember and consider the human. Develop diverse discussions about AI ethics to ensure systems align with societal values.
Ethical guidelines are the fundamental building blocks in establishing trust in AI technologies and technological advancement. We must ensure that AI technology contributes positively to society. We must prevent or at least mitigate potential harm.
As AI continues to evolve, guidelines must also evolve. Both should keep pace with each other. It is not going to be easy for a while, however, many other disruptive technologies have done so successfully in the past. There are already libraries of lessons learned available.
AI Governance Standards
Standards and best practices in AI governance are crucial for ensuring consistency, reliability, safety, and the ethical use of AI technologies across various applications. Here’s a brief look at these elements within the AI governance conversation:
- ISO Technical Standards: ISO/IEC JTC 1/SC 42: The International Organization for Standardization (ISO) focuses on AI standardization. Including aspects such as foundational AI standards, big data, and the trustworthiness of AI systems.
- IEEE P7000 series: The Institute of Electrical and Electronics Engineers (IEEE) developed a set of standards addressing ethical concerns in AI, including transparency, data privacy, and well-being.
- Industry-Specific Standards: IEEE P2807: The Institute of Electrical and Electronics Engineers focused on the use of AI in healthcare to ensure safety and efficacy.
- ANSI/UL 4600: The Underwriters Laboratories (UL) is considered an American Organization, as it is based in the United States and focuses on product safety standards within the American market. This standard provides a framework for the safety of autonomous vehicles.
- Data Standards: There are already a multitude of standards to ensure data quality, interoperability, and security. Data is foundational for AI systems to operate.
- Performance and Testing Standards: Many benchmarks and testing protocols already exist. Extend these to assess AI accuracy, robustness, and fairness.
This article is the result of countless hours of research from a engineering and design perspective. As mentioned previously, the Author has experience with a global manufacturer ($36B annual) that developed disruptive technologies such as GPS, mobile devices and more.
The future is bright and full of potential as next-generation AI services and solutions come to the table. If you want to learn more about what AI can do for your business, just a book an appointment.
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AI Governance Best Practices
- Data Management and Data Governance. Establish clear policies on AI data collection, use, storage, and sharing.
- Data Quality. Ensure that the data utilized for the training of LLM’s is accurate, diverse, and socially representative to avoid biases.
- Model Development and Bias Mitigation. Develop and use techniques that identify and reduce bias in AI algorithms in real time.
- Transparency. Document AI model decisions and methodologies, name those involved and allow for the explanation of the developments.
- Deployment and Operation. Implement continuous monitoring and constantly check on AI systems post-deployment to regularly ensure that they are performing as intended. Note and correct any drift or bias (release notes).
- Human Oversight. Implement smart mechanisms that include the need for human intervention, especially for high-risk decisions.
- Ethical Considerations. Design AI with ethics in mind. Ensure all ethical considerations are implemented at the beginning stages of AI development to ensure it aligns with ethical guidelines.
- Shareholder Engagement and Accountability. Ensure that shareholders (those that will profit from the technology) are aware and involved in AI development and testing phases to ensure alignment with societal values. Hold shareholders accountable for allowing misuse.
- Security Practices. Secure all points within any AI ecosystem against tampering, data breaches, and adversarial attacks. At a minimum, deploy encryption, access controls, and regular security audits.
- Lifecycle Management. This begins at inception and lasts through retirement. Build and manage AI systems with considerations for updates, retraining, and environmental impact. Decommission AI technologies based on performance, ethical concerns, or technological evolution.
- Education and Training. This is possibly the most difficult to achieve. Ensure that all AI developers, engineers, technicians, decision-makers, and more are educated on current AI capabilities and limitations. Ensure that all are aware of the most recent and up to date ethical responsibilities.
AI Standards and Best Practice Challenges and Considerations
Global Variations. Best practices, including the idea of “best efforts”, can vary wildly by country or industry, making global collaboration complex.
Evolving Technology. Considering the fast paced growth of AI, standards and practices need regular updating and quickly.
Balancing Innovation with Regulation. AI developments are moving at a fast pace, traditional regulation methods may stifle creativity and developments.
Implementation. Ensuring that high-level AI guidelines evolve into practical and actionable steps can be challenging.
Practical Implementation. Organizations should seek out AI certifications and independent audits. Regulatory AI entities should have immediate access to these certifications and audits so that companies can demonstrate their compliance (or lack of) with AI standards.
Guidelines from Tech Giants. This is a bit complicated. While companies like Google, Microsoft, and IBM have published their AI principles, corruption may impact their ability to be the trustworthy bedrock of standards for best practices in the AI industry.
The Development of Artificial Intelligence is About Trust.
Legitimate AI standards, and the implementation of honest AI best practices are essential for fostering and nurturing trust in AI technologies. We simply need to ensure, beyond the shadow of a doubt, that AI technology is being developed and deployed in ways that are safe, ethical, and beneficial for society.
Allow humanity the comfort of a set of expectations that span across different sectors and countries working with AI.
AI Risk Management Issues
AI Risk management involves identifying, assessing, and mitigating potential risks associated with the development, deployment and use of AI technologies.
There are many potential types of AI Risks.
- Technical Risks
- Malfunctions or Errors
- Security Vulnerabilities
- Ethical and Societal Risks
- Bias and Discrimination
- Privacy Invasions
- Compliance and Legal Risks
- Regulatory Non-Compliance
- Operational Risks
- Dependency on AI
- Job Displacement
- Strategic Risks
- Innovation Lag
- Reputational Damage
AI Risk Management Best Practices
Due to the dynamic nature of AI, the technology will continue to evolve rapidly, making AI risk management and assessment an ongoing process.
The need to evaluate risks based on their likelihood of occurrence, as well as the potential impact they could have must be considered.
- Risk Identification
- Risk Assessment
- Risk mitigation Strategies
- Monitoring and Review
- Regular Audits
- Incident Response and Recovery
- Preparedness Plans
- Regulatory Compliance
- Legal Reviews
Develop AI-Specific metrics as well as specialized tools to measure aspects like bias, accuracy, and security vulnerabilities.
AI Governance Accountability and Oversight
Accountability and oversight are fundamental components of AI governance. Without accountability and oversight to ensure that AI systems are developed and used responsibly, what mechanism would there be to address the issues, harms, and negative impacts resulting from AI deployment?
We think that these are the top 5 items regarding accountability and oversight in AI.
- Clear Responsibility
Establish clearly defined roles and levels of responsibility. It is imperative to establish who is responsible what and at every stage. There should be no room for “plausible deniability.” Hold those accountable for decisions made at every stage in the development of AI technologies. Extend this responsibility to include to the actions of AI, as well as the outcomes.
- Ethical Accountability
All involved in the development of AI must consider the ethical implications of their AI systems, including transparency, fairness, and respect for privacy. They must ensure that their AI solutions adhere to ethical standards.
- Legal Accountability
We can not allow the development of AI to function like a lawless wild west. Since those from item 1 above already know their roles and level of responsibility, it should be easy to ensure compliance with AI laws such as data protection regulations, consumer rights, and sector-specific regulations. If an AI system violates the laws, there should be legal ramifications for those responsible.
- Accountability for Outcomes
Accountability should include not just the direct outcomes of AI decisions, but also the societal and environmental impacts. Organizations and individuals responsible at the highest levels should be prepared to address and be held accountable for unintended consequences.
- Data Accountability
Data is the ultimate driver of AI technology. Organizations must ensure that the data used by AI systems is sourced ethically and handled responsibly. Add accountability measures for those directly responsible for data quality issues, bias, privacy breaches, misuse and more.
AI Governance Mechanisms for Oversight
To combat the many potential issues surrounding the development of AI, organizations could develop mechanisms for oversight that aid in the accountability process. Ensure that everyone responsible is thoroughly trained. In addition, conduct ongoing testing to ensure the reliability of the training.
Here are a few ideas for mechanisms for oversight regarding the development of AI.
- Internal Oversight
Establish internal ethics boards or committees to oversee AI projects. Not just one board to oversee a plethora of projects, we mean multiple boards for multiple projects. This may help to ensure that each project aligns with ethical guidelines. Develop AI Governance Teams that are specifically tasked with the monitoring of all parts of an AI systems’ performance, compliance, and ethical alignment.
- External Oversight
Streamlined Regulatory Bodies: High speed Government agencies and/or readily available international bodies that regulate AI projects. Giving them the authority to conduct regular audits, enforce compliance, and propose edits to the industry standards quickly.
Third-Party Civilian Agency Audits: Independent auditors with the same access as the streamlined regulatory bodies that can review AI systems and projects. They should be able to enforce rules, as well as propose edits to the industry standards quickly.
- Transparency and Reporting
AI is about the human right? That means it’s about trust. AI Systems should be designed with the intention to publicly explain their decision-making processes and functions. Companies should publicly report various metrics such as failures, usage issues, negative incidents, risks and harm.
- Certification and Standards
We have been discussing adherence to AI Standards all day, they are that important. AI Organizations should follow industry standards and obtain certifications. These can serve as a form of commitment to ensuring best practices.
- User Feedback Loops and Mechanisms
Easily accessible platforms must be developed for AI users to report issues or biases that they encounter with AI systems. We understand that once the human element is introduced, this comes with its own set of issues. However, proper platforms with authentic data can spot negative or faulty trends which can then lead to immediate corrective actions.
- Whistleblower Protections
Whistleblowers within suspect AI organizations must be safe to be able to report known issues. We should develop mechanisms to protect and reward individuals who report known unethical and/or illegal AI practices within organizations. Catching major issues before they are released to the public could eliminate something catastrophic.

AI Accountability and Oversight Challenges
Due to the inherit complexity of AI, determining accountability for AI can be challenging when decisions are made by complex, opaque AI systems. Global Operations add another layer of complexity as AI companies that operate globally face different regulatory environments, thereby complicating AI oversight.
There also has to be a balance between AI innovation and accountability. In America at least, we have all witnessed where oversight has made advancement impossible. We feel that AI is going to overcome these issues in 2025 and beyond.
It is important to ensure that AI oversight doesn’t hinder advancements in AI while also being able to hold responsible parties accountable. Responsibility diffusion management may also be needed. Large organizations may be developing AI systems with multiple parties across the globe. In this scenario, pinpointing responsibility can be difficult.
AI Public Engagement and Transparency
As AI continues to evolve, so must the efforts regarding public engagement and transparency. These are critical aspects of the developmental process considering that AI technology can impact every person on the planet.
We should utilize AI governance to ensure that AI technologies are developed and used in ways that align with public interest, ethical standards, and societal norms. Here are some ideas:
Public Engagement with inclusive Dialogue
Public Consultations
Education and Awareness: We could create programs that educate the public about AI, including its risks and benefits. We could develop workshops and outreach/educational campaigns. We could also integrate AI literacy into school curriculum’s.
Participatory Design
Collaborative Initiatives: Partnerships between government, NGO’s, and tech companies of all sizes to address societal challenges ensuring AI solutions are community-driven.
Transparency
Simple Explainability of AI Systems
Data Transparency
Openness in Development
Reporting and Disclosure: Proactive regular reports and public disclosure on AI systems’ performance, impacts, and incidents to build public trust. This could be further be mandated by laws.
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