Key Considerations for Developing Organizational Generative AI Policies in Credit Unions

Understanding AI Policy

An AI policy communicates required and prohibited activities and behaviors, establishing the broader goals and objectives of the organization. It informs staff of what is allowed and what isn’t. In contrast, standards are mandatory requirements or codes of practice approved by external standards organizations. They provide detailed rules to achieve the policy's intent, particularly in privacy, ethics, and data management.

Pre-Implementation Steps

1. Understand Generative AI: Familiarize yourself with generative AI and its potential applications, risks, and benefits. Know the common types of models, such as ChatGPT and DALL·E. Here are some resources to start: Coursera offers various courses on AI, machine learning, and specifically generative AI. Courses often include modules on the ethical implications and applications of these technologies. America's Credit Unions Training, look for courses and webinars specifically focused on AI and technology use in credit unions. America's Credit Unions often offer industry-specific training that can help you understand how AI can be applied within the context of credit unions.
2. Assess Organizational Needs: Determine how your organization plans to use generative AI, whether for content creation, automation, data analysis, or other functions. This will inform the specific clauses of the AUP.
3. Survey the Regulatory Landscape: Research the legal and regulatory requirements for generative AI in your industry and jurisdiction. This research will influence decisions about your technology environment, including choosing between public and private AI platforms.
4. Conduct a Risk Assessment: Identify potential risks associated with generative AI deployment, including technical risks (e.g., unintended outputs) and ethical concerns (e.g., generating misleading information).
5. Confirm Purpose and Scope: Define the policy’s objective and the scope of systems involved. Specify whether it pertains solely to generative AI or includes other AI models. Articulate the policy's boundaries and intent.
6. Examine Existing IT and InfoSec Policies: Ensure consistency and clarity by checking for potentially overlapping policies. Borrow language and responsibilities from existing policies where appropriate.
7. Engage Stakeholders in Policy Development: To make the policy comprehensive and actionable, include executive leadership, legal teams, technical experts, communication teams, risk management/compliance, and business group representatives.
8. Prepare for Internal and External Communication: Communicate policy updates and changes to employees, customers, and regulators. Keep them informed of your organization’s intended uses, ethics, and privacy surrounding generative AI.
9. Understand Your Technical Environment: Know the technical aspects of the generative AI solutions, including model types, data sources, biases, and capabilities. Decide between public or private AI platforms based on your organization's needs and risk assessments.
10. Utilize a Governance Framework: Implement a governance framework to address ownership, responsibility, and auditing. This ensures consistency, transparency, and accountability in managing AI processes.

Key Considerations for Generative AI Policy Adoption in Credit Unions

1. Policy Scope Impact

Who is impacted by the policy scope? Understanding the impact on members, employees, regulators, and other stakeholders is crucial. Members need assurance that AI systems handle their data securely and ethically. Regulators will require compliance with financial and privacy regulations. Internally, staff must comprehend the policy’s rationale and urgency to ensure adherence. Effective communication about the policy's objectives, such as enhancing member services or operational efficiency, is vital.

2. Generative AI Responsibilities

What are your managers, employees, and IT department’s generative AI responsibilities? Clearly define roles and responsibilities for AI usage. Managers should ensure compliance and provide necessary training. Employees must adhere to guidelines and report any issues. The IT department should maintain secure AI systems and handle data responsibly. Specific duties should include monitoring AI usage, ensuring data privacy, and regularly updating AI tools to address new risks.

3. AI System Security

Are the AI systems secure? Security is paramount in protecting member data and maintaining trust. Implement multi-factor authentication, encryption, and access controls to safeguard AI systems. Regular security assessments and updates are essential to mitigate vulnerabilities. Ensure AI systems comply with financial regulations and privacy standards, protecting against unauthorized access and misuse.

4. Ethical AI Principles

Have ethical AI principles been addressed in the policy? Incorporate ethical AI principles to prevent harm and bias. Establish guidelines to avoid biases in AI outputs and ensure transparency in decision-making processes. Designate someone within the organization to explain AI algorithms and their impact. Regularly review and update ethical guidelines to address new challenges and maintain digital trust with members.

5. Acceptable Use Terms

What does good behavior look like, and what are the acceptable terms of use? Define acceptable and unacceptable behaviors for AI tool usage. Limit AI tools to business-related purposes, adhering to ethical standards and privacy regulations. Specify prohibited activities, such as using AI to generate misleading content. Clear guidelines help maintain a responsible AI usage culture within the credit union.

6. Data Handling and Training Guidelines

What guidelines are in place for data handling and training? Establish strict guidelines for sourcing and handling data, especially personal or sensitive information. Emphasize the use of de-identified and anonymized data to protect member privacy. Ensure high-quality data to improve the accuracy and reliability of AI outputs. Regularly train staff on data handling best practices and update training programs to address new data protection challenges.

7. Transparency and Attribution

How will this policy encourage transparency and attribution? Mandate disclosure of AI-generated content when shared or published. Encourage the use of watermarks or other indicators to identify AI-generated material. Establish procedures for reviewing and validating AI outputs to ensure accuracy and ethical standards. Transparency in AI content creation and attribution builds trust with members and stakeholders, ensuring they understand the origin of the information.

8. Legal and Compliance Requirements

How will your organization ensure legal and compliance requirements are met? Highlight the need to comply with local, national, and international laws regarding AI use. This includes financial regulations, data protection laws, and measures to combat misinformation. Regularly review and update the AI policy to align with evolving legal requirements. Conduct audits and assessments to ensure ongoing compliance. Legal adherence minimizes the risk of penalties and enhances the organization's reputation.

9. Limitations and Risks

What are the limitations and risks involved? Acknowledge the inherent limitations of generative AI models. Provide guidance on when not to rely solely on AI outputs, emphasizing the importance of human oversight. Identify potential risks, such as unintended biases or inaccurate content generation, and develop strategies to mitigate these risks. Clear communication about AI limitations helps manage expectations and ensures responsible AI use.

10. Policy Integration

How does this policy link to others already in place? Ensure the generative AI policy is integrated with existing policies, such as data privacy, information security, and risk management policies. Highlight how these policies support each other to provide a comprehensive framework. Cross-referencing related policies help stakeholders understand the broader context and ensures consistency in policy enforcement. An integrated approach optimizes policy coverage and enhances organizational governance.

11. Exception Handling

How will you highlight exception handling? Define the process for handling unique cases where exceptions to the AI policy may be necessary. Establish clear criteria for approving exceptions and outline the approval process. Document all exceptions and regularly review them to ensure they remain justified. This approach ensures flexibility while maintaining control over AI usage.

12. Reporting and Investigation

How will you report and investigate violations? Provide IT with tools to monitor and investigate AI policy violations. Establish a reporting mechanism for employees to report suspected violations. Outline the investigation process and potential consequences for policy breaches. Ensure transparency in handling violations to maintain trust and accountability. Regular audits and reviews help identify and address policy breaches effectively.

13. Policy Management and Auditing

Who will review, manage, and audit? Designate ownership of the policy and assign responsibilities for its management and auditing. Establish a schedule for regular policy reviews and updates to keep it current. Implement auditing processes to ensure compliance and effectiveness. Regularly update the policy to address new developments in AI technology and regulatory changes. Clear management and auditing protocols ensure the policy remains relevant and effective.

14. Stakeholder Feedback

How and when will the policy be updated? Create a process for stakeholders to provide feedback on the policy. Encourage continuous improvement by incorporating stakeholder input. Regularly update the policy to address new challenges and opportunities. Ensure the policy remains flexible to adapt to changing regulatory landscapes and technological advancements. Continuous engagement with stakeholders helps maintain a dynamic and effective AI policy.

As credit unions navigate the rapidly evolving landscape of generative AI, developing comprehensive and tailored AI policies is essential for ensuring ethical, responsible, and secure use of these powerful technologies. By understanding the financial sector's unique needs and regulatory requirements, credit unions can harness the benefits of generative AI while mitigating potential risks.

Implementing a well-defined generative AI policy protects member data, enhances operational efficiency, and reinforces trust and transparency with members and regulators. It empowers credit unions to leverage AI for innovative solutions, improving member services and maintaining a competitive edge in the financial industry.

Credit unions can create robust frameworks that guide the responsible use of AI by considering the key aspects outlined in this article—such as policy scope impact, AI system security, ethical principles, data handling guidelines, and legal compliance. Engaging stakeholders, integrating policies, and ensuring continuous updates and feedback are critical for keeping the policy-relevant and effective in an ever-changing technological environment.

A strong generative AI policy ultimately positions credit unions to capitalize on AI advancements while upholding their commitment to member-centric values, security, and ethical standards. By taking proactive steps today, credit unions can confidently navigate the future of AI and continue to provide exceptional value to their members.