AI Evaluation and Governance

Building Trust and Equity: Implementing an AI Governance Framework in Education

The integration of Artificial Intelligence (AI) into education promises personalized learning experiences, data-driven insights, and automated tasks. However, with this potential comes the responsibility to ensure its ethical and responsible application. To achieve this, a robust AI Governance Framework tailored to the education sector is paramount.
Drawing inspiration from the AIGA AI Governance Framework and adapting it to educational needs, let’s explore key governance tasks in specific categories

AI System

  • Centralized Repository: Establish a central repository logging all AI systems used in your organization. This facilitates oversight, management, and comparison.
  • Detailed Documentation: Document each system’s targeted use cases, technical architecture, potential risks, and expected impacts on diverse stakeholders (students, teachers, parents). Transparency builds trust and allows scrutiny.
  •  Clear Approval Process: Create a structured approval process for new AI systems, considering ethical implications, alignment with learning objectives, and potential risks.
  • Version Control and Health Checks: Implement strict version control and conduct regular health checks to ensure performance, security, and adherence to initial goals.

Data Operations

  •   Transparency in Data Handling: Document all data preprocessing steps, data quality assurance procedures, and data sourcing methods. This fosters trust and allows for scrutiny.
  •   Data Quality Metrics: Define and monitor relevant data quality metrics like completeness, accuracy, and representativeness. This minimizes biases in outputs.
  •  Data Quality Monitoring and Health Checks: Establish continuous data quality monitoring processes and regular data health checks to identify and address issues promptly.

Risk and Impacts

  • Identifying and Mitigating Risks: Conduct thorough risk assessments for each AI system, documenting potential harms and impacts on different stakeholders (students, teachers, and communities).
  • Non-Discrimination Assurance: Implement processes to assure non-discrimination and fairness in AI systems, including bias detection and mitigation strategies.
  • Transparency, Explainability, and Contestability (TEC) Expectation Canvassing: TEC requirements canvassing amongst the identified stakeholders and understanding their TEC expectations.

Transparency, Explainability, and Contestability (TEC)

  • TEC Monitoring: Design and document a TEC monitoring process to assess the transparency, explainability, and contestability of AI systems used in education.
  •  TEC Health Checks: Conduct regular TEC health checks to gather feedback on the understandability and fairness of AI decisions and address any concerns.

Accountability and Ownership

  •  Clearly Defined Roles and Responsibilities: Assign clear roles and responsibilities for each AI governance task to specific individuals or groups within the education organization.
  •  Accountability Mechanisms: Establish accountability mechanisms to ensure adherence to ethical principles and compliance with governance policies. This could include regular reporting, internal audits, or independent oversight.

Development and Operations

  • Development Process Documentation: Thoroughly document the development process for each AI system, including design choices, data selection, and training procedures.
  • Operations and Monitoring Documentation: Document all operational procedures and monitoring processes for AI systems, ensuring transparency and responsible management.
  • Process for Retiring and Safe Disposal of AI Systems and Data: Establish a clear process for retiring AI systems and for the safe disposal of data, ensuring responsible data management and privacy protection.


  • Regular Audits: Conduct regular internal and external audits to assess compliance with ethical guidelines, data privacy regulations, and other relevant legislation.
  • Regulatory Risk Assessment: Evaluate and address potential regulatory risks associated with AI use in education to ensure compliance with existing and evolving regulations.
  • Compliance with Regulations: Stay up-to-date with evolving regulations on AI and data privacy, adapting governance practices accordingly to maintain compliance.

By implementing these AI governance tasks, we can build a framework that prioritizes ethics, transparency, and accountability in education. This fosters trust, safeguards learners, and ensures AI empowers rather than disadvantages. Remember, this is a starting point. Continuously refine and improve your framework to stay ahead of emerging challenges and ensure AI serves as a positive force in shaping the future of education.


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