AI Policy: From Principles to Legislation is a structured, practical course covering AI policy, EU AI Act, US AI policy, Governance, Regulation. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
Policy & civic techTool stack
D3
Technology marks for AI Policy: From Principles to Legislation, sourced from the CC0-licensed Simple Icons project.
Use AI policy appropriately in a realistic, bounded task.
Use EU AI Act appropriately in a realistic, bounded task.
Use US AI policy appropriately in a realistic, bounded task.
Use Governance appropriately in a realistic, bounded task.
Course outline and revision prompts
Module 1
AI policy: Public problems
Apply AI policy through stakeholders, institutions, evidence.
Lesson
Key terms
Revision question
Stakeholders with AI policy
stakeholders, AI policy, policy
In AI Policy: From Principles to Legislation, which evidence best supports a stakeholders result produced with AI policy?
Institutions with EU AI Act
institutions, EU AI Act, policy
In AI Policy: From Principles to Legislation, which evidence best supports a institutions result produced with EU AI Act?
Evidence with US AI policy
evidence, US AI policy, policy
In AI Policy: From Principles to Legislation, which evidence best supports a evidence result produced with US AI policy?
Instructional figureDecision matrix
Stakeholders: from context to evidence
Stakeholders connects public need and evidence to a accountable outcome in AI Policy: From Principles to Legislation.
1Public need and evidence
Define the purpose, intended user and AI policy constraints.
frames
2Stakeholders
Map who decides, implements, benefits, pays and challenges.
produces evidence for
3Accountable outcome
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
EU AI Act: Digital methods
Apply EU AI Act through open data, services, participation.
Lesson
Key terms
Revision question
Open data with Governance
open data, Governance, policy
In AI Policy: From Principles to Legislation, which evidence best supports a open data result produced with Governance?
Services with Regulation
services, Regulation, policy
In AI Policy: From Principles to Legislation, which evidence best supports a services result produced with Regulation?
Participation with AI policy
participation, AI policy, policy
In AI Policy: From Principles to Legislation, which evidence best supports a participation result produced with AI policy?
Instructional figureContinuous cycle
Open Data: from context to evidence
Open Data connects public need and evidence to a accountable outcome in AI Policy: From Principles to Legislation.
1Public need and evidence
Define the purpose, intended user and Governance constraints.
frames
2Open Data
Check licence, provenance, updates and disclosure risk.
produces evidence for
3Accountable outcome
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Open Data is credible only when the result can be traced back to its purpose, inputs and constraints. Open Data is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 3
US AI policy: Policy design
Apply US AI policy through options, impact, implementation.
Lesson
Key terms
Revision question
Options with EU AI Act
options, EU AI Act, policy
In AI Policy: From Principles to Legislation, which evidence best supports a options result produced with EU AI Act?
Impact with US AI policy
impact, US AI policy, policy
In AI Policy: From Principles to Legislation, which evidence best supports a impact result produced with US AI policy?
Implementation with Governance
implementation, Governance, policy
In AI Policy: From Principles to Legislation, which evidence best supports a implementation result produced with Governance?
Instructional figureSide-by-side comparison
Options: from context to evidence
Options connects public need and evidence to a accountable outcome in AI Policy: From Principles to Legislation.
1Public need and evidence
Define the purpose, intended user and EU AI Act constraints.
frames
2Options
Include capacity, distribution and a no-action baseline.
produces evidence for
3Accountable outcome
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Options is credible only when the result can be traced back to its purpose, inputs and constraints. Options is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 4
Governance: Accountability
Apply Governance through measurement, transparency, maintenance.
Lesson
Key terms
Revision question
Measurement with Regulation
measurement, Regulation, policy
In AI Policy: From Principles to Legislation, which evidence best supports a measurement result produced with Regulation?
Transparency with AI policy
transparency, AI policy, policy
In AI Policy: From Principles to Legislation, which evidence best supports a transparency result produced with AI policy?
Maintenance with EU AI Act
maintenance, EU AI Act, policy
In AI Policy: From Principles to Legislation, which evidence best supports a maintenance result produced with EU AI Act?
Instructional figureContinuous cycle
Measurement: from context to evidence
Measurement connects public need and evidence to a accountable outcome in AI Policy: From Principles to Legislation.
1Public need and evidence
Define the purpose, intended user and Regulation constraints.
frames
2Measurement
Avoid vanity indicators and report distribution.
produces evidence for
3Accountable outcome
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Measurement is credible only when the result can be traced back to its purpose, inputs and constraints. Measurement is the decision layer between the starting context and evidence that the result is fit for purpose.
Course practical outcome
Produce a reviewable AI Policy: From Principles to Legislation project using AI policy, EU AI Act, US AI policy.
Expected output: A working AI Policy: From Principles to Legislation artefact plus an evidence-based self-review.
Tools: A suitable AI policy 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 AI policy.
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 AI policy scope.
Glossary
AI policy
A core concept or tool used in AI Policy: From Principles to Legislation; its exact meaning is established in the relevant lesson.
EU AI Act
A core concept or tool used in AI Policy: From Principles to Legislation; its exact meaning is established in the relevant lesson.
US AI policy
A core concept or tool used in AI Policy: From Principles to Legislation; its exact meaning is established in the relevant lesson.
Governance
A core concept or tool used in AI Policy: From Principles to Legislation; its exact meaning is established in the relevant lesson.
Regulation
A core concept or tool used in AI Policy: From Principles to Legislation; 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.