AI Governance & Implementation

• AI Policy and Governance is essential for mitigating risks, ensuring compliance, and protecting community interests in AI deployment.
• Small towns and counties can adapt AI governance to suit budget constraints and local priorities.

Why AI Governance Matters

• Prevents biased decision-making in public services like law enforcement, housing, and hiring.
• Ensures AI systems are used transparently and fairly for grant management, emergency response, and tax assessments.
• Protects citizens' data privacy when using AI for permit processing or digital records.

Principles of AI Governance

• Fairness and non-discrimination prevents AI from favoring or disadvantaging specific groups.
• Transparency and explainability ensures AI decisions are understandable and justifiable.
• Privacy and security ensures citizens' data is protected from misuse.
• Human oversight is important as AI should assist, not replace, human decision-making.
• Accountability ensures there is clear responsibility for AI-related outcomes.

Understanding Trustworthy AI

• AI should be ethical, reliable, and aligned with legal requirements.
• Trustworthy AI is robust, unbiased, and operates transparently.
• AI should help public sector officials make fair, data-driven decisions while avoiding harm.

Core Pillars of Trustworthy AI

• Safeguard sensitive citizen data.
• Fairness and bias mitigation is important.
• Monitoring and evaluation is a key component.
• Avoid inequalities and ensure fair treatment.
• Conduct frequent and regular assessments.
• Transparency and explainability need to implemented.
• Human-AI collaboration for best results.
• Ensure public trust in AI implementations.
• AI should keep a "human in the loop".
• Implement security and data protection.

Building an AI Governance Review Team

• Define roles - Clarify who is in charge of what tasks.
• Diversify the Team - Include members from more than just the IT Department.
• Establish a review process including data input and AI output.

Roles and Responsibilities in AI Governance

• AI Ethics Officer(s) ensure ethical compliance.
• Data Protection Officers oversee citizen data privacy.
• Compliance and Risk Experts assess AI legal and operational risks.
• AI Developers and IT Dept ensure technical implementation follows policy guidelines.
• Subject Matter Experts ensure data development and data accuracy.

Key Components of an AI Governance Policy

• Purpose and Scope - Define where AI will be used and why.
• Ethical AI Principles – Establish fairness and transparency requirements.
• Risk Management Framework - Plan for potential AI failures and legal risks.
• Audit and Compliance Procedures – Ensure AI systems remain accountable and fair.
• Public Engagement and Transparency. – Create feedback mechanisms for citizens.

Implementing an AI Governance Policy

• Conduct initial AI risk assessments before deployment.
• Define compliance monitoring procedures to prevent misuse.
• Train staff ethics and governance practices.
• Establish continuous review mechanisms to adjust AI policies.
• Ensure accountability at all levels, from mayors to IT staff.

High-Level Risk Management Framework

• Identify Risks – Understand potential AI system failures, biases, and security threats.
• Assess Risks – Evaluate likelihood and impact of risks on operations and community trust.
• Mitigate Risks – Implement bias audits, security patches, and ethical oversight.
• Monitor and Review – Continuously track AI performance and update policies as needed.
• Respond and Adapt – Have incident response plans for unexpected AI failures.

Risks in AI Implementation for Local Governments

• Onsite vs. Offsite Data Storage Risks – Data security concerns arise when outsourcing AI services.
  • Data and system security is important no matter who hosts the AI.
• User Management and Control Risks – Assigning the right staff with the right level of permission and access.
• Knowledge Document Risks – AI must be trained on accurate, unbiased government records.
• IT & AI Hosting Risks – Using a single vendor for both IT and AI can create dependency risks.

GovAl User & Data File Management

• UCPCOG is working with GovAl to customize regional permissions and access levels
• Folio types include project folios, special access, departmental folios, departments general, organizational folios, and access for all users
• Key guidelines for using folios
  • Define objectives for the folio
  • Start with high quality data
  • Organize for easy retrieval
  • Be diligent with sensitive information
  • Ensure data freshness is maintained
  • Test for accuracy
  • Monitor usage
  • Communicate purpose and limitations

GovAI

• Folios help reduce errors/hallucinations by using accurate resources
• Folios give the AI specific context

Folio Timeline and Roadmap

• Introductory webinar today
• Beta access begins now
• Access scoped to specific user, wider adoption to follow
• Feedback and assistance is appreciated
• GovAI Roadmap
  • Refining based on Feedback
  • Refining Crawlers
  • SCIM for Group Management
  • Personal Folios
• Q1 Work
  • Audio File Transcription
  • Teams Bot
  • Smart Prompts
  • Groundwork for citizen engagement

AI Compliance

• AI in government must comply with:
  • Guidelines such as NIST risk management framework
  • Privacy laws such as GDPR and CCPA
  • State-specific privacy laws
  • Transparency and accountability mandates
• Compliance audits ensure that AI policies align with legal and ethical standards

AI Chatbots Case study

• A city deployed AI chatbots for citizen inquiries leading to:
  • 40% call center workload reduction
  • Faster response times
  • Accessibility for non-English speakers

###AI Ethics

• Purpose: monitor, review, and guide AI policies in local government
  • Committee members should include:
  • Government officials
  • AI, IT, and/or data experts
  • Community members
  • Audits upheld

Future of AI Governance

• AI will continue to expand
  • Key trends:
    • Increased federal and/or state regulations
    • More AI powered public services (chatbots, smart city tech)
    • Increased citizen involvement

Implementation Challenges

• Budget constraints
• Technical Expertise
• Regulatory Uncertainty
• Public Resistance

Overcoming AI challenges

• Seek funding from federal and states
• Train existing staff
• Adopt AI solutions
• Engage with AI experts

AI Training

• AI Literacy
• Workshops and certifications
• Talent development
• Elected officials

Common Hurdles

• Lack of transparency
• Bias in AI Models
• Ignoring public input
• Weak oversight

Best Practices for AI

• Vendor transparency
• Ethical policies
• Data privacy agreements and standards

Measuring Al programs success

• Measure using:
  • Performance metrics
  • Satisfaction surveys
  • Bias detection reports
  • Operational cost savings

Citizen Participation

• Ensures aligns with community needs and values
  • Strategies can include:
    • Community town halls
    • Online surveys
    • Transparency reports

Ensuring Inclusivity

• AI Powered Language Translation
• Accessible Al Interfaces
• Equitable Service Distribution
• AI Policy Review

Summary & Call to action

• Al governance ensures fair, transparent, and ethical Al use in government
  • Includes ongoing education and interagency collaboration
  • Implement oversight
  • Commit to continuous improvement
  • Evaluate current AI policies recognizing any gaps