It can be intimidating to begin with AI governance. AI lifespan, human oversight, transparency, and conformance evaluation are only a few of the many new phrases that businesses are using. This is why it seems that each region or standard defines them slightly differently.
The ISO/IEC 42002 standard was created for just this reason. It makes it easier for everyone to communicate about AI governance, including engineers, compliance officers, and auditors. You can take a look at our ISO 42001 certification guide if you’re interested in how this fits into other AI governance standards:
What Is ISO/IEC 42002 and Why Does It Exist
The International Electrotechnical Commission (IEC) and the International Organization for Standardization (ISO) created ISO/IEC 42002, a standard for definitions and terminology when it comes to the use of Artificial Intelligence in businesses.
There are three main goals that the standard defines. These include:
- Reducing misunderstandings between technical and non-technical teams;
- Standardizing the language used in AI governance; and
- Facilitating a more transparent and efficient ISO/IEC 42001 implementation and audit process.
While ISO/IEC 42001 specifies the aspects for an AI Management System (AIMS), ISO/IEC 42002 offers the definitions to guarantee that those aspects are properly understood by all. Think of the ISO/IEC 42001 as “what to do” and ISO/IEC 42002 as “what each term means.”
How ISO/IEC 42002 Supports ISO/IEC 42001 Implementation and Audits
Most organizations frequently fail audits not because they disregard regulations, but because they misinterpret them.
Here is an example to help you better understand this. For instance, let us take comparisons like:
- “AI System” versus “Algorithm”.
- “Continuous Improvement” versus “Monitoring”
- “Control,” “Impact,” and “Risk”
In this regard, the ISO/IEC 42002 guarantees increased clarity surrounding AIMS roles, procedures, and controls. It is guaranteed by ISO/IEC 42002. To add to this, the terminology used by governance teams is also consistent with that of auditors, and policies, audit reports, and risk registers all use the same terminology.
What this does is it lessens conflict and enhances the quality of documents and internal safeguards. It also offers preparedness for certification on top of this.
Governance & Organizational Responsibility Terminology
Organizations need to understand accountability to use AI responsibly.
The standard defines key terms related to AI governance, such as:
| Term | Meaning |
|---|---|
| Governance | Oversight to make sure AI is ethical, secure, and lawful |
| Roles & Responsibilities | Defines who does what in AI accountability |
| AIMS (AI Management System) | The structured framework for running AI responsibly |
| Competence | Having the right skills to manage AI safely |
| Leadership Accountability | Management is responsible for AI's impacts on people |
Here’s why this matters:
- Accountability gaps often lead to compliance failures.
- Regulators increasingly require senior leadership ownership of AI risks.
AI System and Lifecycle Terminology
It is important to understand that even after deployment, AI continues to evolve. This is why it is important to understand the vocabulary related to the AI lifecycle.
| Stage | Purpose |
|---|---|
| Design | Plan and define AI purpose, stakeholders, and risks |
| Development | Data preparation, model training, validation |
| Deployment | Integrate into real-world environments |
| Operation | Use, performance monitoring |
| Modification | Retraining, updates |
| Retirement | Safe decommissioning and data controls |
Apart from these definitions, here are some other common definitions that also include:
- An AI System: It is a software that predicts, decides, or automates tasks.
- Model: The part that uses data to identify trends
- Data Governance: It is how data is managed, safeguarded, and verified.
AI Risk, Controls, and Assessment Vocabulary
This is the core of compliance, and that is precisely why any potential misinterpretation can prove to be expensive.
Here is a look at the key terms that are defined in ISO/IEC 42002 about AI risk terminology:
| Term | Meaning for AI governance |
|---|---|
| Risk | Possibility of harm to people, systems, or society |
| Impact | Severity of that harm |
| Likelihood | How probable the risk event is |
| Risk Controls | Measures taken to reduce risk |
| Residual Risk | Risk left after controls are applied |
If you still want a deep dive into risk management in AI as a beginner, it is important to understand what AI Risk Management under ISO/IEC 23894 is all about.
Here is why knowing the right vocabulary under AI risk, control, and assessment matters:
- AI has the potential to cause operational or system malfunctions, fall prey to social bias, or compliance violations.
- Risk terminology is also important to show due attention to these issues.
Transparency, Human Oversight, and Ethical Terminology
It is extremely important that AI be human-readable, comprehensible, and reviewable. With this in mind, here is a look at some of the important terms you need to be aware of:
| Term | Simple explanation |
|---|---|
| Transparency | People know how and when AI is being used |
| Explainability | Ability to understand why AI made a decision |
| Human Oversight | A person can supervise, review, and correct AI |
| Fairness | AI outcomes treat all users without unjust bias |
| Accountability | Someone is legally and ethically responsible |
There are several ways through which businesses can ensure that there is transparency and human oversight in the standard. With these definitions in place, governance can align better with:
- Global regulations like the EU AI Act and the NIST AI RMF.
- Ethical principles like privacy, dignity, human rights, etc.
Technical Assurance Terminology
To ensure AI continues to behave as intended, there is specific technical assurance terminology that ISO uses. These include:
| Term | What it means in practice |
|---|---|
| Validation | Confirm the system meets requirements before deployment |
| Verification | Check system performs correctly throughout its lifecycle |
| Monitoring | Ongoing performance and risk checks |
| Data Quality | Data must be accurate, complete, and secure |
| Robustness | AI can handle unexpected inputs without failing |
| Security Controls | Protect from attacks and model manipulation |
The main aim of such a strong assurance vocabulary is to ensure that AI remains safe, compliant, and resilient, even as models continue to evolve.
Common Confusions and Misinterpretations in AI Governance Vocabulary
Though there are quite a few distinctive terms in AI governance vocabulary, they can sometimes get confusing and be misinterpreted. Here is a look at some of these and why they matter.
| Confusing Terms | Real Meaning | Why It Matters |
|---|---|---|
| AI vs Algorithm | An algorithm is just a set of rules — AI learns patterns | Using the wrong term can misstate the compliance scope |
| Monitoring vs Logging | Logging collects data; monitoring analyzes it | Monitoring is required by ISO — logging alone isn’t enough |
| Bias vs Discrimination | Bias exists in all data; discrimination causes harm | Only harmful bias triggers accountability actions |
| Audit vs Assessment | Audits provide official certification; assessments help prepare | Using the wrong word can mislead regulators |
The main purpose of the ISO/IEC 42002 standard is to remove ambiguity, an essential aspect when facing certification bodies.
How Organizations Can Use ISO/IEC 42002 to Standardize Language
Here is a look at some of the main starting steps for organizations to help use ISO/IEC 42002 to standardize language. :
Adding definitions:
Begin by defining specific aspects or terms. This could include:
- AI governance policies
- AIMS documentation
- Data and model governance procedures
Next, train teams on consistent vocabulary. This is because:
- Developers essentially always use governance language
- Auditors use the same definitions as implementers
- Legal and risk teams speak a unified terminology.
Introduce the usage of shared glossaries. This should ideally be done in:
- Risk registers
- Audit evidence packs
- AI lifecycle documentation
When everyone uses the same words, governance becomes faster and stronger.
Benefits of Using ISO/IEC 42002 in AI Governance Programs
Here is a look at some of the most important benefits of using ISO/IEC 42002 in AI Governance Programs.
| Benefit | Impact |
|---|---|
| Better communication | Less confusion between technical, legal, and executive teams |
| Audit readiness | Faster ISO/IEC 42001 certification success |
| Regulatory alignment | Terminology matches global AI governance expectations |
| Stronger risk controls | Clearer reporting and accountability |
| Trust and ethics | Users and regulators can rely on transparency |
You see, one of the main aims of the ISO/IEC 42002 is to directly improve operational maturity and certification outcomes.
Future Role of ISO/IEC 42002 as AI Regulation Evolves
AI regulations are rapidly expanding, and this is particularly with regard to certain aspects of the AI world. These include:
- Usage of high-risk AI systems.
- Use of biometrics in the workplace
- Safety-critical choices
To compare requirements, the industry and governments need a standardized nomenclature. The ISO/IEC 42002 standard is, thus, expected to:
- Comply with the terminology of the EU AI Act
- Encourage the labeling and conformance evaluation of AI systems
- Give terms related to supply-chain liability and trust.
- Minimize disagreements over the interpretation of governance.
Essentially, this guideline will keep closing the gap between corporate accountability and requirements for regulations and public confidence.
Final Thoughts
It is always a better idea to learn the terminology if your company is just getting started with AI governance. This is essentially because every subsequent process, including documentation, risk assessments, and audits, is made simpler by it.
Your common lexicon for responsible AI is ISO/IEC 42002.
This standard can benefit you by effectively comprehending the regulations, making communication more understandable, and cultivating trust with regulators and stakeholders.
We, at GAICC, can assist you if you’re looking for professional instruction and certification in ISO/IEC 42001 implementation and AI governance terminology.
