Lesson 1 of 12

Structured learning draft

Stakeholders with EU AI Act

In AI Law, Policy & Regulation, the way a learner handles stakeholders shapes how EU AI Act is used and evaluated. Stakeholders use, operate, govern or are affected by a system. This beginner lesson focuses on a decision or output that another person can inspect.

Learning objectives

Explain stakeholders in the context of AI Law, Policy & Regulation.
Apply EU AI Act to a bounded practical task.
Evaluate the result using explicit quality criteria.

Instructional figureDecision matrix

Stakeholders: from context to evidence

Stakeholders connects affected people and context to a documented mitigation in AI Law, Policy & Regulation.

Affected people and context

Define the purpose, intended user and EU AI Act constraints.

frames

Stakeholders

Map power, benefit, burden and ability to contest.

produces evidence for

Documented mitigation

Compare the observed result with a normal case, boundary case and stated limitation.

Takeaway: Stakeholders is credible only when the result can be traced back to its purpose, inputs and constraints. Stakeholders is the decision layer between the starting context and evidence that the result is fit for purpose.

Stakeholders use, operate, govern or are affected by a system. For EU AI Act, distinguish performing an operation from demonstrating that it suits the stated purpose. Map power, benefit, burden and ability to contest. Record assumptions that could change the conclusion.

Apply stakeholders deliberately

State the AI Law, Policy & Regulation task and the decision it supports.
Prepare a small EU AI Act case with a known input and difficult boundary.
Map power, benefit, burden and ability to contest.
Compare the observed result with the expected behaviour and explain differences.
Save the evidence, limitation and next action in a review record.

stakeholders evidence record
Review point	Evidence
Purpose	The specific EU AI Act outcome and intended user
Method	The stakeholders decision, input and version or context
Result	Observed output plus a checked boundary case
Limitation	What the result does not establish and the next safe action

Common mistakes

Using EU AI Act before defining what stakeholders must achieve.
Checking only the easiest AI Law, Policy & Regulation example.
Reporting a result without its input, assumptions or limitation.

Practice activity

Apply the lesson

For AI Law, Policy & Regulation, complete a bounded EU AI Act task demonstrating stakeholders. Keep the original input, numbered method, normal test, boundary test, observed results and a 100-word self-review naming one limitation and next improvement.

Check your understanding

In AI Law, Policy & Regulation, which evidence best supports a stakeholders result produced with EU AI Act?

The input, expected behaviour, normal and boundary results, and a limitation A polished EU AI Act output without its input A screenshot showing the tool opened A claim that the method always works

Lesson summary

For AI Law, Policy & Regulation, stakeholders means: Stakeholders use, operate, govern or are affected by a system.
A credible EU AI Act result includes a checked boundary, not only a successful example.
The next lesson builds on this stakeholders evidence record.

Sources and further reading

Recommendation on the Ethics of AIUNESCO - accessed 2026-08-21
AI PrinciplesOECD - accessed 2026-08-21

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