Conceptual role of Practice Notes & Guidance Papers in AI governance
This page outlines, in neutral and generic terms, how Practice Notes and Guidance Papers may function as non-binding materials that help professionals interpret AI governance concepts in practice through the 2020s and 2030s. It is not a catalogue of official IIAIG publications, not legal advice and not a regulatory standard.
- Explains, at a conceptual level, what “practice notes” and “guidance papers” can do in an AI governance ecosystem, including in future, more structured regulatory environments.
- Does not announce, list or endorse any concrete documents, and does not create binding obligations or confer any special status on IIAIG or other bodies.
- Encourages careful distinction between non-binding practice materials and laws, regulations or formal technical standards, which remain the responsibility of regulators and standard-setters.
What are Practice Notes & Guidance Papers in AI governance?
In many professional fields, non-binding practice materials help practitioners translate high-level principles and frameworks into day-to-day judgment. In AI governance, Practice Notes and Guidance Papers can play a similar conceptual role: they offer structured examples, decision factors and implementation perspectives without becoming law or regulation, and can be updated over time as AI capabilities and regulatory expectations evolve.
Translating frameworks into practice
Practice Notes can offer worked examples, scenario discussions and practical checklists that show how AI governance principles and frameworks might be applied in typical situations, while explicitly acknowledging that context and applicable regulation matter and may change over the next decade.
Supporting professional judgment
Rather than prescribing one “correct” answer, guidance papers can highlight factors to consider, questions to ask and trade-offs to document, assisting – but not replacing – professional and institutional judgment, including decisions about when to seek legal or specialist advice.
Encouraging iterative learning
Over time, non-binding notes can be revised to reflect new experience, regulatory developments or emerging good practices, while clearly documenting their version, scope and status, and retiring or consolidating older notes where appropriate.
Any organization using such materials remains responsible for complying with applicable laws, regulations and binding standards in its jurisdiction, both now and as new AI-related requirements emerge.
How Practice Notes sit alongside principles, standards and FAQs
The table below provides a conceptual positioning of different types of documents that may appear in an AI governance ecosystem. It is generic and does not refer to specific publications, regulators or standard-setting processes.
| Document type (conceptual) | Typical role | Binding nature | Illustrative contents |
|---|---|---|---|
| Principles / codes | Articulate high-level values and commitments (for example, fairness, accountability, human oversight) that guide AI governance. | May be binding within an organization or profession, depending on adoption and enforcement mechanisms. | Short statements of principle, duty-based expectations and broad standards of conduct. |
| Frameworks / standards | Provide structured models for managing AI risks and governance across the lifecycle. | May be referenced in regulation or contracts, or voluntarily adopted; binding nature depends on legal and contractual context. | Definitions, roles, processes, control families, assurance expectations, documentation patterns. |
| Practice Notes & Guidance Papers | Offer non-binding, practical interpretation of principles and frameworks in specific themes or scenarios. | Conceptually non-binding; their value relies on clarity, quality and professional acceptance, not legal compulsion. | Scenario walk-throughs, questions to ask, option analysis, indicative decision trees, documentation examples, “do/don’t” considerations. |
| FAQs / explainer materials | Address common questions in accessible language for broader audiences (for example, students, staff, clients). | Typically explanatory; binding nature, if any, arises from underlying policies or contracts referenced. | Plain-language answers, diagrams, summary charts and links to more detailed documents. |
Organizations often use a combination of these document types; the distinctions above are conceptual and may overlap in practice.
Suggested structure for an AI governance Practice Note
The table below describes a neutral, suggested structure that institutions may adapt when drafting Practice Notes and Guidance Papers related to AI governance. It is not a mandatory template; future practice may add machine-readable elements or structured metadata on top of these narrative sections.
| Section | Purpose | Illustrative elements |
|---|---|---|
| 1. Scope & audience | Clarify who the note is for and which AI-related situations it addresses. | Short description of intended users (for example, risk officers, course leaders, product managers) and in-scope scenarios or decision points. |
| 2. Context & references | Position the note relative to existing frameworks, policies and regulations. | References to internal policies, laws or standards; explicit statement that the note is non-binding and does not override them; links to relevant AI governance frameworks. |
| 3. Key questions & decision factors | Highlight the main questions practitioners should consider. | Lists of questions, decision tables or “if/then” prompts focused on AI governance, risk and ethics topics, including when to seek escalation or specialist review. |
| 4. Illustrative scenarios | Show how the questions and decision factors might be applied. | Fictional or anonymized scenarios, with narrative examples of options considered and trade-offs recorded; notes on how context or regulation could change conclusions. |
| 5. Documentation & evidence | Encourage appropriate record-keeping in line with governance expectations. | Suggestions for what to record (for example, rationale, mitigations, approvals), and where such records should be stored; references to any AI system registries or risk logs used. |
| 6. Limitations & review | Acknowledge limits of the note and describe review cadence. | Statements on what the note does not cover, when it will be reviewed, and how feedback can be provided; indications of jurisdictional or sectoral limits. |
The exact layout, length and level of detail will vary according to audience, sector and institutional preferences, and may become more structured as AI governance documentation practices mature.
Conceptual lifecycle & versioning of Practice Notes
Non-binding documents can still benefit from disciplined lifecycle management. The table below describes a conceptual approach to drafting, adoption, review and retirement, which can also evolve toward more data-driven documentation in the 2030s.
| Stage | Illustrative activities | Key considerations |
|---|---|---|
| 1. Drafting | A working group prepares an initial draft, drawing on relevant expertise and existing policies. | Clear authorship; alignment with organizational tone and legal constraints; explicit non-binding status; avoidance of promises that resemble guarantees or warranties. |
| 2. Consultation | Target users and governance bodies are invited to comment on clarity, feasibility and alignment. | Managing expectations; documenting how feedback is handled; avoiding scope creep into policy-level decisions that require separate approval. |
| 3. Publication | The note is published with a version number, date and clear indication of its status (for example, “non-binding guidance”). | Accessibility; cross-references to policies; avoiding ambiguity about whether the note is mandatory; in future, potentially adding machine-readable metadata for registries or tools. |
| 4. Review & update | Periodic reviews consider regulatory developments, feedback and practical experience; revisions are versioned. | Maintaining a change log; sunsetting outdated guidance; signalling when significant changes occur; coordinating with risk, compliance and training functions so updates are reflected in practice. |
| 5. Retirement | Notes that are no longer appropriate are archived, with references to superseding materials where applicable. | Avoiding confusion by clearly marking retired documents and retaining them only for historical or audit purposes, in line with records management policies. |
Institutions may integrate this lifecycle into broader documentation and records management policies, and adapt it as AI governance and assurance practices become more standardized.
How Practice Notes may evolve in a 2030s AI governance ecosystem
Looking ahead, practice-oriented materials may play a more visible role in connecting everyday AI decisions with formal governance, assurance and regulatory expectations. The cards below present a neutral, forward-looking orientation – not predictions or commitments.
Living knowledge systems
Practice Notes could form part of living “knowledge systems” for AI governance – connected to internal AI registries, risk logs and training materials – with structured ways for practitioners to provide feedback and for governance bodies to see how notes are used in practice.
More structured, machine-readable elements
Over time, organizations may add machine-readable tags, risk levels or control mappings to practice notes so that AI governance tools – for example, assessment workflows or dashboards – can surface relevant guidance automatically, while the narrative text continues to support human judgment.
Cross-institutional learning, with care
Where appropriate and lawful, anonymized insights from practice notes and their use may feed into sector-wide learning – for example, through professional institutes or networks – while respecting confidentiality, competition rules and regulatory boundaries.
Any such evolution would require careful governance, transparent scoping and alignment with applicable laws, standards and institutional strategies.
Conceptual thematic areas for AI governance Practice Notes
Without proposing specific documents, the cards below illustrate thematic areas where organizations might find Practice Notes and Guidance Papers helpful, both today and in more mature AI governance regimes.
Notes that illustrate how to assign AI responsibilities across board, management, risk, compliance and technical teams, consistent with existing governance structures and evolving AI regulations.
Guidance on conducting AI impact assessments, including indicative question sets, escalation triggers and documentation suggestions, aligned with internal risk taxonomies and applicable regulatory expectations.
Examples of how human decision-makers interface with AI tools, including oversight expectations, override logging, training implications and considerations for worker well-being and workload.
Practical notes on data minimization, access control, de-identification strategies and record-keeping in AI contexts, anchored in applicable data protection and confidentiality rules, and responsive to future regulatory refinement.
Guidance on monitoring AI performance, responding to incidents and documenting model changes or decommissioning decisions, including linkages to internal incident management and audit functions.
Notes aimed at curricula designers, trainers and supervisors on integrating AI governance themes into education and continuing professional development for technical and non-technical roles.
Each organization or professional community will identify themes most relevant to its context, regulatory environment and strategy.
Example (fictional) titles of Practice Notes & Guidance Papers
The list below contains fictional, illustrative titles only. It does not refer to real publications or commitments by any organization. Its purpose is to show how scope and naming might be expressed.
- “Practice Note: Documenting Human Overrides in AI-Enabled Decision Processes (Conceptual Orientation)”
- “Guidance Paper: Considerations for AI-Related Student Support Tools in Higher Education (Illustrative Factors)”
- “Practice Note: Questions to Ask Before Procuring Third-Party AI Services (Neutral Checklist)”
- “Guidance Paper: Conceptual Approaches to Bias Testing in AI-Assisted Workflows (Non-Binding Examples)”
- “Practice Note: Integrating AI Risk Themes into Existing Enterprise Risk Registers (Orientation Only)”
Any real document should include its own disclaimers, scope statements and references to applicable legal and policy frameworks.
What this Practice Notes & Guidance Papers page does – and does not – represent
To keep expectations clear, it is important to differentiate conceptual orientation from binding standards, legal advice or accreditation.
What this page does
- Offers neutral language and structures for thinking about Practice Notes and Guidance Papers in AI governance.
- Highlights how such materials can support – but not replace – professional judgment and institutional governance.
- Encourages careful versioning, lifecycle management and clear scoping for any practice-oriented documents, with a view to more mature AI governance ecosystems.
What this page does not do
- Does not list, endorse or certify any actual practice notes, guidance papers or standards.
- Does not establish IIAIG as a regulator, standard-setter, accreditation body or supervisory authority.
- Does not constitute legal, regulatory, investment or risk advice in any jurisdiction.
- Does not create legal or contractual obligations for any institution or individual.
Institutions remain responsible for interpreting and complying with the laws, regulations and binding standards that apply to them, and should seek qualified advice where necessary.
Using this orientation for your own Practice Notes
Governance, risk, legal, ethics, academic and technical leaders can use this page as a conceptual reference when planning non-binding AI governance guidance materials, always anchored in their institutional and regulatory context and adaptable to future AI governance developments.
Any concrete Practice Note or Guidance Paper should be drafted, reviewed and approved through your institution’s established governance and documentation processes.