AI Policy

1. Data as a Strategic Asset: The County recognizes data as a valuable asset that can drive informed decision-making, improve operations, and enhance public services.
2. Data Quality: The County is committed to maintaining high data quality standards, ensuring accuracy, completeness, consistency, and timeliness of data.
3. Data Security and Privacy: The County prioritizes the protection of data confidentiality, integrity, and availability through robust security measures and compliance with applicable privacy regulations.
4. Data Accessibility and Sharing: Data should be accessible only to authorized users for legitimate purposes, while ensuring appropriate controls and restrictions to protect sensitive information.
5. Data Accountability: Data owners and stewards are responsible for the management and governance of their respective data assets.
6. Data Governance Framework: A comprehensive data governance framework will be established to oversee the implementation and enforcement of this policy.

• Data Governance Council: An executive-level Data Governance Workforce will be formed to provide strategic direction and oversight for data governance activities.
• Data Stewardship Roles: Data owners and stewards will be assigned to manage specific data assets, ensuring their quality, security, and appropriate use.
• Data Standards and Policies: Consistent data standards, definitions, and policies will be developed and enforced to ensure data consistency and interoperability. Following best practices in classifying data, a comprehensive Data Classification Framework will be developed to identify and categorize data based on its sensitivity, confidentiality, and criticality. Exemplary levels are included below:
  • Level 1 (Public Data): Information intended for public access that poses minimal risk if disclosed.
    • Examples: Published reports, public records, website content, press releases.
  • Level 2 (Internal Data): Non-sensitive information accessible to internal County employees but not intended for public distribution.
    • Examples: Drafts of policies, internal procedural documents, non-public building layouts.
  • Level 3 (Confidential Data): Information restricted to authorized personnel only, where unauthorized disclosure may cause material harm.
    • Examples: Employee records, non-public financial information, and data with personal identifiable information (PII).
  • Level 4 (Highly Confidential Data): Highly sensitive data requiring strict access controls due to its potential to cause significant harm if disclosed.
    • Examples: Social Security Numbers, financial account details, health/medical information, infrastructure/secure facility drawings/building plans, and encryption keys.
• Data Management Processes: Effective data management processes will be implemented for data collection, storage, retrieval, analysis, and disposal leveraging Data Classification Framework. Departments will classify their data assets and ensure appropriate safeguards, access controls, and handling processes are implemented in accordance with classification level.
• Data Security and Privacy Controls: Robust security measures will be implemented to protect data from unauthorized access, use, disclosure, disruption, modification, or destruction.
• Data Quality Management: Data quality assurance processes will be established to monitor and improve data accuracy, completeness, consistency, and timeliness.
• Data Sharing and Collaboration: Guidelines for data sharing and collaboration will be developed to facilitate information exchange while protecting sensitive data.

• Data Quality: Ensure data accuracy, completeness, and consistency.
• Data Bias: Mitigate bias in data and algorithms.
• Ethical Considerations: Adhere to ethical guidelines for data use and AI development.
• Transparency and Accountability: Be transparent about data sharing practices and AI models.
• Continuous Improvement: Regularly evaluate and improve data sharing processes and analytics capabilities.

Abstract Text for AI Implementation: Learn more about Miami-Dade County’s approach to ethical and responsible AI implementation, featuring use cases and toolkits designed to empower our workforce and residents, and best practices.