From Values to Policy: The Principles Behind AI Governance
AI in education offers huge opportunities, but also risks such as privacy breaches, bias and vendor dependency. In Part 1, we saw that governance provides the structure: who decides and who is responsible.
In this article (Part 2) we go one step further: on what basis are those decisions made? The answer: principles. These form the moral and legal compass that allows schools to create policies that are not only technically sound but also ethically and pedagogically robust.
TL;DR - Summary
- School AI policies: should be grounded in five core principles: transparency, privacy & data governance, fairness & equal opportunities, robustness & safety, and sustainability.
- These principles: make policies explainable, testable and legitimate.
- Without principles: AI use lacks direction and trust.
The Five Core Principles
1. Transparency & Explainability
Why: AI systems often act as black boxes. Without explanation, trust among pupils, parents and teachers erodes.
What this means in policy:
- Tools must be able to explain how results are produced in accessible terms.
- Vendors should provide model cards or clear documentation.
- The boundaries and limitations of AI must be actively communicated.
Example: Our adaptive learning platform is based on previous test results but cannot account for illness or home circumstances. Teachers remain ultimately responsible.
Read more about transparency in AI decision-making - what schools should demand from vendors, and how explainability contributes to fair and trustworthy education.
“Policy without values Is rudderless.”
2. Privacy & Data Governance
Why: schools process highly sensitive data, and AI increases risks of breaches or misuse.
What this means in policy:
- Collect only the data strictly necessary (data minimisation).
- Store and process data within the EU where possible, or ensure strict legal safeguards (e.g. Standard Contractual Clauses) if data is transferred abroad.
- Vendors must sign a Data Processing Agreement (DPA).
Example: AI tools may only be used if all pupil data is stored within the EU or with equivalent safeguards, and is automatically deleted at the end of the school year.
Read more about protecting privacy in school-based AI - what happens to the data entered into AI tools, and how schools can safeguard pupils' personal information responsibly.
3. Fairness & Equal Opportunities
Why: AI can reinforce inequality if datasets contain bias.
What this means in policy:
- Test tools for bias and representativeness.
- AI must never independently decide on progression, selection or assessment.
- Human oversight must always be guaranteed.
Example: AI advice for placement in advanced classes may be used as input, but the final decision always rests with the teacher.
Read more about fairness and equal opportunities in AI - how algorithms can unintentionally discriminate, and what schools can do to ensure AI decisions remain fair for all pupils.
4. Robustness & safety
Why: AI systems can fail, crash, be hacked, or produce unpredictable responses - with serious consequences for pupils and educational decisions.
What this means in policy:
- AI tools must be not only technically stable but also consistent in their output: similar input should not produce widely varying responses.
- AI must not make final decisions without human review or confirmation.
- Mandatory fallback plans and manual alternatives for critical applications.
- Vendors must undergo annual security audits.
- Teachers must actively check AI output for errors, bias, or random inconsistencies.
Example:: If ChatGPT is used to assess open-ended answers, it must be clear that identical input yields consistent results. A teacher always retains final responsibility for the assessment.
Read more about robustness and safety in AI - why schools need dependable, consistent AI tools that teachers and pupils can trust, and how technical stability underpins quality and fairness in education.
5. Sustainability & Responsibility
Why: AI consumes significant energy and resources. Sustainability is not yet a legal requirement, but it is increasingly recognised as a vital principle for long-term responsible digital education.
What this means in policy:
- Preference for vendors using CO2-neutral or renewable-powered data centres.
- Conduct cost-benefit analyses including Total Cost of Ownership (TCO).
- Reuse and recycle equipment where possible.
Example: In procurement, we compare not only price and functionality but also ecological footprint and lifecycle impact.
Read more about the ecological footprint of AI - how every prompt and quiz consumes energy, the hidden environmental costs of AI in education, and how schools can reduce their digital impact through conscious choices and sustainable strategies.
From Principles to Policy: The Translation
| Principle | Concrete rule | Testable condition |
|---|---|---|
| Transparency | Every AI tool must include a model card explaining logic & limits | Documentation is understandable for non-tech users |
| Privacy | No tools without EU storage or equivalent safeguards | Vendor signs a DPA and specifies datacentres |
| Fairness | AI may never be the sole basis for progression or selection | A teacher always has final responsibility |
| Robustness | Critical applications must have fallback procedures | Backup plans are documented and tested |
| Sustainability | Prefer vendors with green datacentres | Vendor provides CO2-report or certification |
Balancing Principles and Dilemmas
Principles guide decisions, but in practice they can conflict:
- Transparency vs. Usability: full explainability can make tools less practical.
- Solution: require minimum explainability plus a one-page summary for teachers.
- Innovation vs. Safety: rapid adoption can clash with thorough privacy checks.
- Solution: start with small pilots before scaling up.
- Cost vs. Sustainability: greener solutions often cost more.
- Solution: consider long-term savings and available subsidies.
Conclusion
Principles are the bridge between vision and rules. They ensure AI policy is not only legally sound but also ethically and pedagogically rooted.
In short:
- Principles are the compass for AI policy.
- Without them, policies lack legitimacy and trust.
Part of a Series
This article forms Part 2 (Principles) of the series AI Governance and AI Policy in Education:
- Part 1 - Foundation: Why Governance Is Essential
- Part 2 - Principles: From Values to Policy (this article)
- Part 3 - Practice: AI Policy in Action
Together, these articles give school leadership, ICT coordinators and teachers guidance to use AI responsibly and effectively.