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Artificial intelligence (AI) is in the early stages of transforming industries, but with great opportunity comes great risk. How can we balance the innovative opporutnity of AI with the risks that this evolving tech brings?
Governance is a system of policies, practices, and principles that organizations use to steer towards long-term goals. Governance over AI needs to be developed and deployed responsibly, balancing innovation with ethical considerations and risks.
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What Is Governance?
Governance refers to the process of control through systems, enabling organizations to steer decisions effectively. The term originates from the Greek word kubernetes, meaning “the person who steers a boat.”
Similarly, governance involves using structured systems, such as policies and leadership oversight, to guide organizations in the right direction. It operates at the highest level—such as boards of directors or executive leadership—to influence long-term direction and ensure alignment with the ogranization's vision, core values, ethical standards, and legal requirements.
Governance of AI
AI governance specifically addresses the unique risks and opportunities of artificial intelligence. It goes beyond immediate operational decisions to consider broader implications, such as ethical values, societal norms, and long-term business goals.
Key considerations include:
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Privacy: Protecting personal data used by AI systems.
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Bias and Inclusion: Ensuring fairness and preventing discrimination in AI algorithms.
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Transparency: Providing explanations of how AI decisions are made.
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Accountability: Holding individuals or governing bodies responsible for AI outcomes.
Governance ensures AI systems align with organizational goals and societal expectations while mitigating risks like misuse or unintended consequences.
Key AI Governance Frameworks
Governance frameworks are not laws. They are not requirements, but rather guidelines. Several frameworks have emerged to help organizations navigate AI governance:
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The AI Bill of Rights (White House, 2022): This U.S. blueprint outlines five key principles:
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Safe and effective systems
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Protection against algorithmic discrimination
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Data privacy
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Notice and explanation
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Alternative human fallback systems
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OECD AI Principles (2019, updated 2024): Adopted globally, these principles emphasize:
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Inclusive growth and well-being
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Human-centered values and fairness
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Transparency and explainability
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Robustness, security, and safety
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Accountability
Recommendations include investing in AI research, fostering inclusive ecosystems, and promoting international cooperation.
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NIST AI Risk Management Framework (2023): Developed by the National Institute of Standards and Technology, this framework offers tools for assessing AI risks, measuring progress, and integrating AI governance into broader organizational strategies.
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ISO 42001 (2023): This international standard establishes a framework for AI Management Systems (AIMS), guiding organizations to align AI initiatives with ethical principles and ensure continuous improvement.
Why AI Governance Matters
AI governance is critical for ensuring that organizations benefit from AI's potential while guarding against its risks. By prioritizing ethical practices, organizations can build trust with stakeholders, comply with regulations, and achieve sustainable growth.
Governance frameworks offer a roadmap for managing AI's complexity and ensuring it serves the long-term interests of both organizations and society. As trusted advisors, we can use frameworks as high-level guidelines for establishing organizational policies and practices around the use of AI.
Conclusion
As AI continues to evolve, so must our approaches to governance. Frameworks like the AI Bill of Rights, OECD Principles, and ISO standards provide valuable guidance for navigating this dynamic landscape. Exercising judgment and adapting these frameworks becomes the challenge and opportuntiy for today's CPAs and CISAs.
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