Fayston Preparatory School: G7 Hiroshima AI Process (HAIP) Transparency Report

I have developed an original classification system tailored for K–12 education, defining “unreasonable risks” as those that pose unacceptable threats to student safety, institutional credibility, or public trust. Risks are categorized into ethical (e.g., algorithmic bias), operational (e.g., system misuse), and systemic (e.g., governance failure) types, informed by the OECD AI Principles and the G7 Hiroshima AI Process Code of Conduct.

Although the framework has not yet been deployed, I have designed a lifecycle-based risk evaluation process, including pre-use rubrics, misuse scenario logs, and oversight workflows. These are intended to help institutions identify and monitor vulnerabilities, misuse, and emerging risks in school contexts before and after AI system deployment.

I have structured a red-teaming methodology focused on educational AI systems. This includes simulation of edge cases such as biased feedback loops, adversarial misuse, and unintended student interactions. These tests are intended to be implemented during pilot phases and reviewed by independent advisors.

I have developed both qualitative tools (e.g., stakeholder interviews, perception audits) and quantitative methods (e.g., deviation scoring, impact thresholds). Reporting mechanisms are designed to be multilingual, anonymous, and inclusive. While no monetary incentives are in place yet, the framework encourages recognition-based disclosures via student and staff governance channels.

Yes. I have consulted with international accreditation and AI policy experts in the development phase. The framework includes pathways for independent third-party reporting, along with secure intake protocols to ensure confidentiality and transparency.

Yes. The framework is explicitly aligned with the OECD AI Principles, the G7 Hiroshima AI Process Code of Conduct, and informed by best practices from UNESCO, NIST, and ISO/IEC standards. It is designed to translate these technical and policy frameworks into practical tools for school-level governance.

I am in the process of establishing cross-sector partnerships to pilot the framework. It is designed to support collaboration between school leaders, AI developers, policymakers, and accreditation bodies. Mitigation strategies will be co-developed during implementation, grounded in real institutional needs.

This is an original, independently developed governance framework authored by me, Timothy Kang. It is currently pre-deployment and was created to fill a policy gap in the application of international AI principles to school systems. I welcome collaboration with OECD and G7 stakeholders to pilot, refine, and scale this contribution.

I have designed an AI governance framework that outlines clear mitigation protocols for each stage of the AI lifecycle: pre-deployment (design and consent checks), deployment (limited-use environments and monitoring), and post-deployment (incident tracking and audits). Each phase includes documented steps for addressing foreseeable vulnerabilities, particularly in educational AI contexts.

Testing measures such as scenario analysis, misuse simulations, and ethical stress testing are built into the framework as tools to surface latent risks. Once piloted, these will guide mitigation through model revision, policy updates, and adjusted access or usage permissions.

The framework prescribes testing in restricted sandboxed environments prior to any live deployment. These secure environments will simulate school use cases, preventing real-world impact during the testing phase. All tests will be logged and reviewed by governance teams.

Data protocols emphasize contextual relevance, de-identification, and auditability. The framework includes safeguards to prevent bias amplification through diverse data review panels, documented provenance, and policy alignment with OECD fairness principles. While no data has yet been collected or processed under this framework, the structures are in place.

The framework emphasizes strict use of licensed, open-source, or institutionally owned datasets. All AI-assisted tools will be required to respect copyright constraints, with detailed guidance provided to school leaders and developers on identifying, attributing, and avoiding unauthorized content use.

I have designed this framework to operate under strong privacy-first principles. Student and staff data must be anonymized, access-limited, and compliant with applicable data protection laws. Governance protocols include user consent tracking, redaction procedures, and internal audits to prevent inadvertent data disclosure.

i. The framework includes a cybersecurity risk checklist based on ISO/IEC 27001 and NIST AI RMF. These tools are intended to be used during vendor review and internal system evaluation stages.

ii. While I do not currently manage proprietary model weights, the framework calls for restricted-access storage, encrypted cloud environments, and multi-factor authentication for all sensitive elements in schools using third-party or open AI models.

iii. A staged vulnerability log and triage system is designed to flag, classify, and escalate risks to an oversight team, with external support if necessary. Risk resolution protocols are outlined in detail for future implementation.

iv. The framework mandates quarterly review of AI tools and security protocols by designated school governance teams, and external audit every 12 months once deployed.

v. Yes, a policy is drafted within the framework that defines misuse monitoring, role-based access controls, and internal reporting pathways for potential insider risk—particularly relevant in educational staff usage scenarios.

A proactive incident management structure is included, featuring a disclosure hotline, stakeholder communication guidelines, and a classification framework for prioritizing emerging risks. While not yet active, this system is ready for deployment during the pilot phase.

This security and risk management protocol is part of an original, independently developed governance framework authored by me, Timothy Kang. It is designed for integration into educational institutions and accreditation systems, and aligns fully with HAIP principles. I welcome collaboration to test and refine the model in live pilot settings.

I. I have developed comprehensive templates and reporting structures as part of my AI governance framework. While they have not yet been deployed, the framework is designed to produce and maintain clear technical documentation and usage guidelines for advanced AI tools used in education.

II. Major system updates or vendor changes will trigger an immediate addendum to the documentation, which must be reviewed by governance teams before deployment.

III. The framework is designed to include:

• Evaluation results for safety, security, and societal risks
• Risk assessments related to human rights, fairness, and bias
• Red-teaming/test results for readiness
• Documentation of capabilities and limitations
• Instructions for safe and appropriate use

The framework outlines a stakeholder communication protocol for educational AI deployments, including shared reporting with accrediting agencies, peer institutions, and policy research networks. I intend to release white papers and participate in forums such as OECD.AI, GPAI, and MSA as part of its dissemination strategy.

Yes. I have drafted a set of privacy guidelines covering student and teacher data, AI-generated outputs, and prompt history. These are written to align with FERPA, OECD Data Governance Principles, and emerging AI-specific privacy standards, and will be published alongside system documentation.

While I do not personally train foundation models, the framework includes mandatory disclosure requirements for any vendor-provided systems, including training data sources, labeling practices, and data enrichment procedures. Schools adopting third-party AI tools will be required to make this information publicly accessible.

Yes. The framework includes a transparency index for each AI tool used, governance dashboards for school boards, and opt-in/opt-out features for users. I have also structured feedback loops through student councils, staff committees, and community forums to ensure multi-stakeholder transparency beyond documentation alone.

This section is based on an independently developed, pre-deployment AI governance framework designed by me, Timothy Kang. It is built to model transparency as a core value of institutional AI adoption in K–12 systems and is intended for international sharing, piloting, and policy alignment under HAIP and OECD efforts.

I have developed a governance framework that integrates AI risk management into school-level leadership structures, accreditation workflows, and policy review cycles. Risk policies are designed to be reviewed quarterly and updated in response to emerging threats, regulatory changes, or audit findings. The framework includes predefined triggers for policy revisions, such as incidents, new deployments, or material vendor updates.

Although not yet implemented, the framework includes a comprehensive training protocol for school leaders and faculty. It features role-specific guidance, annual refreshers, onboarding briefings for new staff, and scenario-based exercises to promote governance fluency in AI contexts.

Yes. The framework is designed to include a public-facing AI governance statement, user rights documentation, and risk management summaries published through school websites, accreditation reports, and parent/community briefings. These communications are intended to build institutional transparency and public trust.

Yes. The framework includes an internal incident management protocol that requires all steps—from report intake to resolution—to be logged in a secure risk registry. Templates for incident documentation, analysis, and resolution tracking are built into the system for consistent accountability.

A tiered sharing protocol is built into the framework. Critical incidents would be escalated to governance boards and external evaluators. Summaries of non-sensitive incidents are intended for inclusion in public risk updates, and emerging trends would be shared with peer institutions and policy networks where applicable.

Yes. The framework includes a policy for responsible disclosure of significant incidents to stakeholders such as accrediting bodies, education ministries, and peer organizations. While privacy is prioritized, non-identifiable summaries will be shared publicly to contribute to collective learning and risk prevention.

I intend to publish insights and implementation guides through international forums (e.g., OECD.AI, UNESCO, GPAI), as well as contribute to accreditation and education leadership networks. The framework is designed to support case study publication and inter-school collaboration on AI governance.

Yes. This framework is grounded in best practices drawn from the OECD AI Principles, G7 Hiroshima AI Process Code of Conduct, UNESCO’s AI in Education recommendations, and the NIST AI Risk Management Framework. It adapts these standards to fit the operational realities of schools and educational institutions.

This section reflects an original, independently authored governance framework created by me, Timothy Kang. Though pre-deployment, it was intentionally built to scale across schools globally and contribute meaningfully to the advancement of transparent and responsible AI risk governance.

As part of my independently developed governance framework for education systems, I have integrated a requirement for AI-use disclosure protocols. These include clear user-facing indicators—such as labels, prompts, and usage disclaimers—whenever users interact with or receive output from advanced AI systems. These disclosures are designed to appear within learning platforms, parent notifications, and student interfaces to ensure transparency, particularly for minors in school environments.

Yes. Although the system is pre-deployment, I have included in the framework a policy requiring AI-generated content to be labeled or watermarked, where technically feasible. This includes instructional content, assessments, and communications generated by AI tools. These mechanisms are designed to comply with emerging international standards, such as W3C provenance models, ISO/IEC 23053, and OECD recommendations. In addition, all AI-generated materials used in classrooms are expected to carry metadata or visual markers to distinguish them from human-created content.This section reflects provisions within an original, pre-deployment governance framework authored solely by me, Timothy Kang. It is designed to meet and extend HAIP-aligned best practices for AI transparency and provenance in the education sector and will evolve alongside future global content authentication standards.

I have developed a governance framework that explicitly integrates research-backed criteria for fairness, explainability, and AI trustworthiness in K–12 education. Although the framework is not yet deployed, it incorporates safeguards such as bias impact rubrics, explainability checklists, and documentation protocols intended to strengthen institutional AI safety. The framework is grounded in ongoing review of global best practices and is structured to inform future investment and iterative refinement.

While the framework is currently self-funded and independently authored, I actively contribute thought leadership through my involvement in international policy and accreditation forums. I am seeking collaboration with institutions, researchers, and technology developers to pilot the provenance labeling and audit mechanisms embedded in the framework—especially in contexts involving minors, educational integrity, and instructional content verification.

Yes. As a Commission Representative for an international accreditation body and a member of multiple governance networks (including Center for AI Safety), I participate in cross-sectoral working groups that explore scalable AI safety policies. My framework is intended to serve as a use-case for integrating AI governance into school evaluations and to catalyze further research into context-sensitive risk mitigation tools.

The framework is designed with a focus on equitable access and digital inclusion, particularly in under-resourced K–12 environments. It emphasizes transparency in algorithmic decision-making that may affect student placement or performance. While environmental metrics are not yet embedded, future iterations will consider carbon-aware AI adoption policies for schools using cloud-based or compute-intensive systems.

This framework is an original, pre-deployment initiative authored independently by me, Timothy Kang. It is positioned to serve as a globally scalable governance reference for schools and accreditation systems, offering a values-driven, education-specific contribution to OECD and G7 priorities in AI safety, fairness, and societal responsibility.

I have developed a governance framework aimed at reducing inequality in AI access, safeguarding student rights, and ensuring ethical oversight in education—especially in international schools and emerging economies. The framework promotes cost-effective, low-compute AI applications in learning environments, with future iterations planned to incorporate environmental sustainability metrics to guide responsible procurement and infrastructure usage.

Yes. The framework embeds AI literacy modules for students, faculty, and leadership. These include policy briefings, ethical simulation workshops, and responsible use protocols. I have also contributed to national and international digital literacy conversations and am preparing guidance aligned with UNESCO's AI competencies for educators. Once piloted, this initiative will provide a replicable model for school-based AI literacy worldwide.

Yes. This framework directly supports SDG 4 (Quality Education), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 16 (Peace, Justice, and Strong Institutions). It promotes human-centric governance, safeguards against biased or opaque AI decision-making, and ensures that AI use in schools respects the dignity and rights of all learners. It was developed as a policy-to-practice bridge for human-centric and inclusive AI adoption.

While formal pilots are pending, I have designed the framework to be adaptable in collaboration with ministries of education, accreditation bodies, and civil society actors concerned with equity in tech adoption. Ongoing dialogues with governance experts and academic networks help ensure the framework remains inclusive, cross-cultural, and globally relevant.

This final section reflects my commitment—as the sole author of this framework—to shaping AI governance through the lens of educational equity, human rights, and long-term sustainability. I welcome collaboration with OECD, G7, and UN-aligned partners to advance this work from concept to implementation.