AI Policy and Governance: What You Need to Know

Dedicated AI policies, in addition to other management frameworks and documentation, help organizations more effectively manage AI governance, data usage, and ethical best practices. AI governance — the practice of monitoring, regulating, and managing AI models and usage — is being rapidly adopted by organizations around the globe.

In this guide, we’ll dissect AI policy and governance in greater detail, explaining how a comprehensive AI policy that emphasizes all areas of AI governance can lead to more manageable, explainable AI and a more ethical and successful operation as a whole.

What Is an AI Policy?

An artificial intelligence policy is a dynamic, documented framework for AI governance that helps organizations set clear guidelines, rules, and principles for how AI technology should be used and developed within the organization.

Creating an AI policy should help your business leaders clarify and highlight any ethical, legal, or compliance standards to which your organization is committed, as well as identify the “who,” “what,” “when,” “why,” and “how” for strategic AI usage that aligns with overall organizational goals and strategies.

Every organization’s AI policy will look a little different to meet their specific objectives for AI governance, but in general, most AI policies include some version of the following components and structural elements:

• An overarching vision for AI usage and growth in the organization.
• Mission statements, clear objectives, and/or KPIs that align with this vision.
• Detailed information about regional, industry-specific, and relevant regulatory compliance laws as well as other ethical considerations.
• A catalog of approved tools and services that can be used for AI development and deployment purposes.
• Defined roles and responsibilities related to AI usage.
• An inventory and procedure for data privacy and security mechanisms.
• A defined procedure for reporting and addressing AI performance and security issues.
• Standards for AI model performance evaluation.

What Is AI Governance?

AI governance is a group of best practices that includes policies, standardized processes, and data and infrastructure controls that contribute to a more ethical and controlled artificial intelligence ecosystem.

When organizations put appropriate AI governance standards and frameworks in place, training data, algorithms, model infrastructure, and the AI models themselves can be more closely monitored and controlled throughout initial development, training and retraining, deployment, and daily use. This contributes to a more efficient AI operation as well as compliance with relevant data privacy and AI ethics regulations.

Who Manages AI Governance?

AI governance is a complex process, especially if your organization is working with multiple generative AI models or other large-scale AI platforms. The following individuals and teams play important roles in different aspects of AI governance and management:

• Executive Leaders: An organization’s C-suite and other top leaders should establish the overall direction, goals, and vision for AI governance and associated AI policies. Regardless of their specific title, all business executives should be clear on what AI tools are being used and what regulations and policies are in place to regulate that usage.
• Chief Information Officer: Unless your organization prefers to have a chief AI officer or chief technical officer oversee this kind of work, the CIO is the primary business leader who takes broader organizational strategies and goals and applies them to actual AI governance development and implementation. This individual is also responsible for ensuring that AI integrates smoothly and securely with all other technologies and infrastructures in your business’s tech stack.
• Chief Data Officer: The CDO is primarily responsible for data governance and data-level quality assurance. In their role, they work to manage data quality, data privacy and compliance, and transparent data preparation workflows for AI model training sets.
• Chief Compliance Officer and Legal/Compliance Teams: This individual or group of individuals keeps up with international, national, regional, industry-specific, and other regulations that may impact how your organization can use data — including PII and intellectual property — and AI models. If a chief ethics officer works among this team, this work may go beyond simple compliance management and move toward setting up ethical decision-making and training frameworks.
• Data Science, AI, and IT Security Teams: These are the teams that handle the hands-on development tasks for training data, algorithms, models, performance monitoring, and security safeguards. While they may not have a hand in setting AI governance standards, they will likely play the biggest role in carrying out these standards.
• AI Ethics Committee: If your organization has established an AI ethics committee that operates separately from your C-suite executives, these committee members will act as advisors to leadership in establishing governance frameworks that consider AI ethics from all angles, including personal privacy, transparent data sourcing and training, and environmental impact.
• HR and Learning and Development Teams: These leaders are in charge of incorporating AI governance best practices and rules into the recruitment and hiring process so all new members of the team are aware of the roles and responsibilities they have when using AI. This team may not come up with the actual training materials or goals, but because of their background with other types of training, they may be tasked with leading AI usage training across the organization.
• Third-Party Consultants: If your organization chooses to hire third-party consultants for data management, AI development, or strategic planning, these individuals may take over some or all of the other taskwork covered above. However, you’ll want to make sure key stakeholders in your organization work collaboratively with these advisors to create an AI governance policy that is both comprehensive and fitted to your specific needs.
• Government and Industry Regulators: Depending on the industry or region you’re working in, third-party regulators could play a major role in determining what AI governance looks like for your organization, as they establish and enforce rules for ethical AI and data use. Many countries and regional groups like the EU are currently working on more comprehensive AI legislation, so expect this group’s role in AI governance to grow quickly in the coming months and years.

Why Is AI Governance Important?

AI governance is one of the most effective ways to establish, organize, and enforce standards for AI development and use that encourages ethical and compliant practices, transparency, continual monitoring and improvement, and cross-team collaboration.

AI governance can improve AI model usage outcomes and help organizations use AI in a way that protects customer data, aligns with compliance requirements, and maintains their reputation as an ethical operator, not only with their customers but also with their partners and the industry at large.

Establishing an independent AI governance strategy can also help your organization get more out of the AI technology you’re using, as creating this type of plan requires your team to flesh out its AI vision, goals, and specific roles and responsibilities in more granular detail. The accountability that gets built into an AI governance strategy helps to prevent and mitigate dangerous biases, create a plan of action for when AI development or use goes awry, and reemphasizes the importance of maintaining personal data privacy and security.

The Benefits of Having an AI Policy for AI Governance

An AI policy extends several benefits to organizations that are looking to develop a more comprehensive AI governance strategy. These are just a handful of the ways in which a dedicated policy can help you stay on task, compliant, and oriented with your initial vision:

• Structured guidance for all AI tool developers and users: This type of AI policy can act as a user manual for both AI developers and users of these tools, as it considers the entire AI lifecycle, from development to deployment to ongoing monitoring and fine-tuning. The standardized rules that are part of this type of policy facilitate cross-organizational buy-in and help your technical teams create a roadmap for AI best practices in real-world scenarios.
• A mechanism for widespread accountability: AI policies provide documented rules for organizational and role-specific AI best practices. This means that all relevant stakeholders have a point of reference that clearly outlines their roles, responsibilities, procedures, limitations, and prohibitions for AI usage, which helps to avoid both ethical and compliance issues.
• Better adherence to regulatory and data security laws: While the leaders in your organization are likely aware of regulatory and data security laws and how they apply to your business, chances are most other employees could benefit from additional clarification. Enforcing an AI policy that reiterates these laws and how they apply to your organization can assist your compliance and legal teams in communicating and mitigating issues with compliance laws at all levels of the organization.
• Clear outline of data privacy standards and mechanisms: Beyond simply stating data security and compliance expectations, AI policies detail how data privacy works and what mechanisms are in place to protect data when it’s handled, stored, and processed for AI models. This level of detail guides all employees in how they should protect an organization’s most sensitive data assets and also gives the business a clear blueprint for what they should look for and where they should look during AI audits.
• Builds customer trust and brand reputation: As AI’s capabilities and use cases continue to expand, many people are excited about the possibilities while others — probably including many of your customers — are more distrusting of the technology. Establishing an AI policy that enforces AI governance while creating more transparency and explainability is a responsible way to move forward and gives your customers more confidence in how your organization uses AI in its operations.
• Preparation for incoming AI regulations: While few AI-specific regulations have passed into law at this point, several groups, including the EU, the U.K., and the U.S., are working toward more comprehensive AI regulations and laws. Creating a comprehensive AI policy now can help your organization proactively align with AI best practices before they are required in your regions of operation; doing this work now can reduce the headache of reworking your processes down the line.

AI Policy and Governance Best Practices

If your AI policy is not clear on its expectations for AI governance and general use, your teams may run into issues with noncompliance, security, and other avoidable user errors. Follow these best practices to help every member of your team, regardless of how they work with AI, remain committed to high standards of AI governance:

• Pay attention to relevant regulations: Consider important regional, national, and industry-specific regulations and stay up-to-date with your knowledge so AI systems remain in compliance at all times.
• Implement standards for data security and data management: AI is a data-driven technology, so be sure to use appropriate data management tools, strategies, and processes to protect and optimize that asset.
• Cover the entire AI lifecycle in your AI policy: Your AI policy should not simply focus on how AI models are developed or how they are used. Instead, create a comprehensive policy that covers everything from data preparation and training to model creation and development, model deployment, model monitoring, and model fine-tuning.
• Establish ethical use standards and requirements: Keep in mind employee-specific roles and responsibilities and set up role-based access controls or other security standards to underpin those rules and protect your consumers’ most sensitive data. Additionally, pay attention to important concepts like AI bias, fairness, data sourcing methods, and other factors that impact ethical use.
• Create standards for ongoing evaluation of model performance: What will you be looking at when you’re monitoring your models in “the real world”? Your AI policy should detail important performance metrics and KPIs so you can stick to your goals and fairly evaluate performance at all stages of AI usage and development.
• Accompany your AI policy with dedicated user training: To help all employees understand how your AI governance policy applies to their work, provide dedicated user training that covers cybersecurity, ethical use, and other best practices, ideally with real-world scenarios and examples.
• Document and regularly update your AI policies: AI policies should not be static documents; they should dynamically change as tooling, user expectations, industry trends and regulations, and other factors shift over time.
• Communicate your ethical practices to relevant stakeholders and customers: Strategically and transparently communicate your governance standards and details of your policy to third-party investors, customers, partners, and other important stakeholders. This communication strategy helps to establish additional trust in your brand and its ethical approach to AI.

Bottom Line: Using Your AI Policy and Governance Best Practices for Better AI Outcomes

Most businesses are already using AI in some fashion, or will likely will adopt the technology soon to keep up with the competition in their industry. Creating and adhering to an AI policy that covers compliance, ethics, security, and practical use cases in detail not only gives these organizations a more strategic leg to stand on when getting started with large-scale AI projects but also helps them meet customer and legal expectations when using AI technology.

Developing detailed AI policies and governance strategies often feels like an overwhelming process, and especially for organizations that are just dipping their toes into the AI pool, establishing this kind of policy may feel like overkill or a waste of time. But this is the wrong way to look at it; instead, think of your AI governance policy as an insurance policy for the modern enterprise. Especially as AI regulations become more well-defined in the coming months and years, it will pay to have an AI policy that proactively paves the way to more responsible and effective artificial intelligence.

Source : EWeek