AI Law, Policy & Regulation is a structured, practical course covering EU AI Act, GDPR, Policy, Compliance, Governance. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
Responsible AITool stack
OpenAI
Technology marks for AI Law, Policy & Regulation, sourced from the CC0-licensed Simple Icons project.
Use EU AI Act appropriately in a realistic, bounded task.
Use GDPR appropriately in a realistic, bounded task.
Use Policy appropriately in a realistic, bounded task.
Use Compliance appropriately in a realistic, bounded task.
Course outline and revision prompts
Module 1
EU AI Act: Ethical foundations
Apply EU AI Act through stakeholders, harms, rights.
Lesson
Key terms
Revision question
Stakeholders with EU AI Act
stakeholders, EU AI Act, ethics
In AI Law, Policy & Regulation, which evidence best supports a stakeholders result produced with EU AI Act?
Harms with GDPR
harms, GDPR, ethics
In AI Law, Policy & Regulation, which evidence best supports a harms result produced with GDPR?
Rights with Policy
rights, Policy, ethics
In AI Law, Policy & Regulation, which evidence best supports a rights result produced with Policy?
Instructional figureDecision matrix
Stakeholders: from context to evidence
Stakeholders connects affected people and context to a documented mitigation in AI Law, Policy & Regulation.
1Affected people and context
Define the purpose, intended user and EU AI Act constraints.
frames
2Stakeholders
Map power, benefit, burden and ability to contest.
produces evidence for
3Documented 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.
Module 2
GDPR: Evidence
Apply GDPR through bias, measurement, impact.
Lesson
Key terms
Revision question
Bias with Compliance
bias, Compliance, ethics
In AI Law, Policy & Regulation, which evidence best supports a bias result produced with Compliance?
Measurement with Governance
measurement, Governance, ethics
In AI Law, Policy & Regulation, which evidence best supports a measurement result produced with Governance?
Impact with EU AI Act
impact, EU AI Act, ethics
In AI Law, Policy & Regulation, which evidence best supports a impact result produced with EU AI Act?
Instructional figureDecision matrix
Bias: from context to evidence
Bias connects affected people and context to a documented mitigation in AI Law, Policy & Regulation.
1Affected people and context
Define the purpose, intended user and Compliance constraints.
frames
2Bias
Trace disparity to lifecycle decisions.
produces evidence for
3Documented mitigation
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Bias is credible only when the result can be traced back to its purpose, inputs and constraints. Bias is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 3
Policy: Governance
Apply Policy through accountability, documentation, oversight.
Lesson
Key terms
Revision question
Accountability with GDPR
accountability, GDPR, ethics
In AI Law, Policy & Regulation, which evidence best supports a accountability result produced with GDPR?
Documentation with Policy
documentation, Policy, ethics
In AI Law, Policy & Regulation, which evidence best supports a documentation result produced with Policy?
Oversight with Compliance
oversight, Compliance, ethics
In AI Law, Policy & Regulation, which evidence best supports a oversight result produced with Compliance?
Instructional figureProcess flow
Accountability: from context to evidence
Accountability connects affected people and context to a documented mitigation in AI Law, Policy & Regulation.
1Affected people and context
Define the purpose, intended user and GDPR constraints.
frames
2Accountability
Name owners for approval, monitoring and appeal.
produces evidence for
3Documented mitigation
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Accountability is credible only when the result can be traced back to its purpose, inputs and constraints. Accountability is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 4
Compliance: Practice
Apply Compliance through risk review, participation, monitoring.
Lesson
Key terms
Revision question
Risk review with Governance
risk review, Governance, ethics
In AI Law, Policy & Regulation, which evidence best supports a risk review result produced with Governance?
Participation with EU AI Act
participation, EU AI Act, ethics
In AI Law, Policy & Regulation, which evidence best supports a participation result produced with EU AI Act?
Monitoring with GDPR
monitoring, GDPR, ethics
In AI Law, Policy & Regulation, which evidence best supports a monitoring result produced with GDPR?
Instructional figureContinuous cycle
Risk Review: from context to evidence
Risk Review connects affected people and context to a documented mitigation in AI Law, Policy & Regulation.
1Affected people and context
Define the purpose, intended user and Governance constraints.
frames
2Risk Review
Use affected-stakeholder and technical evidence.
produces evidence for
3Documented mitigation
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Risk Review is credible only when the result can be traced back to its purpose, inputs and constraints. Risk Review is the decision layer between the starting context and evidence that the result is fit for purpose.
Course practical outcome
Produce a reviewable AI Law, Policy & Regulation project using EU AI Act, GDPR, Policy.
Expected output: A working AI Law, Policy & Regulation artefact plus an evidence-based self-review.
Tools: A suitable EU AI Act environment, A plain-text decision log, Test data or realistic sample material
Production steps
Define the intended user, outcome and constraints.
Create the smallest complete result using EU AI Act.
Test one normal case, one boundary case and one failure response.
Revise the work from the evidence and preserve before-and-after results.
Prepare a concise handover containing method, limitations and next step.
Success criteria
The output matches the stated outcome.
Inputs and decisions are reproducible.
Boundary and failure evidence is included.
Limitations and responsibility considerations are explicit.
Self-review
Can another learner repeat the method?
Did I test a difficult case?
Did I avoid unsupported claims?
Is the next action proportionate to the remaining risk?
Next step: Choose one weakness found during review and improve it before extending the EU AI Act scope.
Glossary
EU AI Act
A core concept or tool used in AI Law, Policy & Regulation; its exact meaning is established in the relevant lesson.
GDPR
A core concept or tool used in AI Law, Policy & Regulation; its exact meaning is established in the relevant lesson.
Policy
A core concept or tool used in AI Law, Policy & Regulation; its exact meaning is established in the relevant lesson.
Compliance
A core concept or tool used in AI Law, Policy & Regulation; its exact meaning is established in the relevant lesson.
Governance
A core concept or tool used in AI Law, Policy & Regulation; its exact meaning is established in the relevant lesson.
This guide is generated from DigiLearn course material. Product versions, regulations and professional standards can change; consult the linked authoritative source before applying version-sensitive guidance.