Introducing the Updated AI Localism Repository: A Tool for Local AI Governance

In a world where AI continues to be ever more entangled with our communities, cities, and decision-making processes, local governments are stepping up to address the challenges of AI governance. Today, we're excited to announce the launch of the newly updated AI Localism Repository—a curated resource designed to help local governments, researchers, and citizens understand how AI is being governed at the state, city, or community level.

What is AI Localism?

AI Localism refers to the actions taken by local decision-makers to address AI governance in their communities. Unlike national or global policies, AI Localism offers immediate solutions tailored to specific local conditions, creating opportunities for greater effectiveness and accountability in the governance of AI.

What’s the AI Localism Repository?

The AI Localism Repository is a collection of examples of AI governance measures from around the world, focusing on how local governments are navigating the evolving landscape of AI. This resource is more than just a list of laws—it highlights innovative methods of AI governance, from the creation of expert advisory groups to the implementation of AI pilot programs.

Why AI Localism Matters

Local governments often face unique challenges in regulating AI, from ethical considerations to the social impact of AI in areas like law enforcement, housing, and employment. Yet, local initiatives are frequently overlooked by national and global AI policy observatories. The AI Localism Repository fills this gap, offering a platform for local policymakers to share their experiences and learn from one another.

Key Features of the AI Localism Repository

• Governance Measures: The repository highlights a variety of governance measures, which refer to the systems and practices that set the direction for and control how decisions are made. For AI Localism, 'governance' includes actions taken to adapt governments to account for AI use and improve existing decisionmaking around AI through principles, policies, processes, and procurement methods. These include but are not limited to: laws, frameworks, working groups, new government agencies, new government data stewardship positions, pilot programs, etc.

• Sector-Specific Approaches: From public health to smart cities, the repository includes examples from diverse sectors, providing insights into how AI is being integrated into different aspects of public life.

• Mechanisms: Whether it's the establishment of local government agencies, the creation of data stewardship positions, or the launch of AI pilot programs, the repository organizes the methods local governments are using to govern AI into four groups: 

• Principles: Overarching guidelines meant to inform further, specific governance action. These are flexible ideals that provide direction.

• Policies: A clear and actionable directive, usually presented as legislation or regulation.

• Processes: The step-by-step method of implementing the policy.

• Procurement: The systems in place to monitor how tools (i.e. AI) are acquired for use by local public actors.

• Focus: The repository highlights the focus of the AI Localism initiative taken into account. Each focus area reflects a core value that underpins responsible local AI governance, providing a roadmap for how AI can be deployed in a way that upholds the common good. These include: 

• Accountability: The example includes an avenue for assessing the performance of an AI tool to verify its ability to meet requirements.

• AI Ethics: The example includes a directive of adhering to social good ethics and values while using AI.

• Consent: The example includes a feature to obtain consent from potential subjects before obtaining/using their data.

• Explainability: The example includes a directive of using clear, accessible language to describe the AI/algorithm/decision-makinng system/training data.

• Participation and Engagement: The example includes a feature for public feedback in the design, deployment, and audit of an AI tool.

• Privacy: The example includes a method for protecting personal data from being used by an AI tool.

• Procurement: The example includes a system for obtaining and auditing AI tools used by public bodies.

• Responsibility: The example includes a directive of designing and using AI with good intention and ethical values in mind.

• Standardization: The example includes a method of streamlining an AI tool to meet a set standard/criteria.

• Transparency: The example includes a method for sharing information about the decision making system, training data, results, and people involved in the AI tool.

How You Can Get Involved

We encourage you to explore the AI Localism Repository and share it with others who are interested in shaping the future of AI governance at the local level. If you know of any local initiatives we've missed, please feel free to share them with us in the comments section or reach out directly.

🔗 Visit the repository today: AI Localism Repository
💻 Learn more about AI Localism: AI Localism Website
🌍 Contribute: Help us capture more local AI governance developments by sharing your insights! Use the form on our website to share examples of AI Localism or to express your interest in collaborating with us on the AI Localism program.