AI Ethics: Ethical foundations
Apply AI Ethics through stakeholders, harms, rights.
Beginner / Structured learning draft / Version 0.9
Structured learning draftAI Ethics: Principles & Practice
Evaluate technology decisions with evidence and accountability. You will finish with a documented risk and impact assessment.
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Responsible AITool stack
OpenAI
12guided lessons
4focused modules
1reviewable project
PDFoffline workbook
The problem
What this course helps you do
AI Ethics: Principles & Practice is a structured, practical course covering AI Ethics, Bias, Fairness, Accountability, Transparency. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
Who it is for
Product, policy and technical learners evaluating responsible AI.
Prerequisites
- No specialist background required
Portfolio-ready project
Produce a reviewable AI Ethics: Principles & Practice project using AI Ethics, Bias, Fairness.
Deliverable: A working AI Ethics: Principles & Practice artefact plus an evidence-based self-review.
Practice fileTechnology impact reviewA risk, affected-party, evidence and mitigation worksheet.Planning template · MarkdownProject briefDefine the user, outcome, boundaries, evidence and acceptance criteria before building.Printable workbook · PDFComplete course workbookA4-ready lesson prompts, project worksheets, evidence logs and references.
Downloads open without an account. Practice data is fictional; review each file before using it in another system.
Definition of done
- The output matches the stated outcome.
- Inputs and decisions are reproducible.
- Boundary and failure evidence is included.
- Limitations and responsibility considerations are explicit.
Course plan
From first concept to finished work
Bias: Evidence
Apply Bias through bias, measurement, impact.
Fairness: Governance
Apply Fairness through accountability, documentation, oversight.
Accountability: Practice
Apply Accountability through risk review, participation, monitoring.
Source-led learning
Authoritative references
Course claims are grounded in primary documentation and recognized public guidance. Recheck version-sensitive information before professional use.