Responsible AI Governance: How to Balance Innovation with Ethical Use
New research emphasizes that AI governance is about establishing practices and structures to align AI investments and management with an organization's strategic interests.
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As organizations increasingly recognize the importance of responsible AI governance, IT leaders face the challenge of developing policies that appropriately use data and AI models while mitigating organizational risk. Vital to this process is identifying strategic, tactical, and operational decision-making approaches to ensure AI aligns seamlessly with organizational goals and values.
“AI governance provides a framework for the responsible deployment and use of AI, ensuring alignment with the organization’s objectives and values while also adhering to ethical standards and regulatory requirements,” says Irina Sedenko, research director at Info-Tech Research Group. “It provides a structured approach to managing, monitoring, and controlling the effective and human-centric use and development of AI systems.”
Info-Tech Research Group’s research Govern the Use of AI Responsibly With a Fit-for-Purpose Structure highlights the challenges organizations face in integrating AI risks into their overall risk management framework.
Despite having policies for AI use, many organizations lack the necessary controls for monitoring and enforcing compliance. Whether organizations acquire AI-enabled applications, partner with an integrator, or develop models from scratch, they require effective AI governance to ensure the technology’s use is in the organization’s best interest and to mitigate potential risks.
A robust AI governance framework helps mitigate these risks by establishing guidelines and controls that align with the ethical standards and values of the organization.
AI governance constitutes a set of practices and structures aimed at aligning investments, resources, and risks with the organization’s best interests to generate tangible business value. For effective AI governance, this blueprint outlines the following methodology for IT leaders:
Address Responsible AI and AI Risk
- Set expectations on the goals of AI governance, AI risks, and risk management frameworks.
- Assess whether proper AI risk controls are in place.
- Identify AI risk management objectives, metrics, and targets.
- Determine and define responsible AI principles for the organization.
Define the AI Governance Structure
- Identify key elements of the organization’s AI governance structure: strategic, tactical, and operational.
- Define mandate, roles, and responsibilities.
- Identify integration points between AI governance and other governance structures.
Define the AI Governance Operating Model
- Define key AI governance operating model elements.
- Develop the first draft of the AI governance charter.
- Recommend AI governance procedure and policy framework.
- Build an AI Governance Implementation Roadmap.
- Identify AI governance implementation initiatives.
“The AI governance framework and its components are the same whether organizations are developing their own AI systems or integrating AI tools and technologies with AI built into the tool,” says
Andrew Sharp, research director at Info-Tech Research Group. “Organizations believe that if they adopt a ‘buy’ approach for AI systems, AI governance is not required because they have no control over the technology embedded in the products and systems they acquire. However, this is not the case. Implement AI governance to ensure the investments made, the risks accepted, and the resources applied to AI projects align with business value.”
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