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.
Responsible AITool stack
OpenAI
Technology marks for AI Ethics: Principles & Practice, sourced from the CC0-licensed Simple Icons project.
Use AI Ethics appropriately in a realistic, bounded task.
Use Bias appropriately in a realistic, bounded task.
Use Fairness appropriately in a realistic, bounded task.
Use Accountability appropriately in a realistic, bounded task.
Course outline and revision prompts
Module 1
AI Ethics: Ethical foundations
Apply AI Ethics through stakeholders, harms, rights.
Lesson
Key terms
Revision question
Stakeholders with AI Ethics
stakeholders, AI Ethics, ethics
In AI Ethics: Principles & Practice, which evidence best supports a stakeholders result produced with AI Ethics?
Harms with Bias
harms, Bias, ethics
In AI Ethics: Principles & Practice, which evidence best supports a harms result produced with Bias?
Rights with Fairness
rights, Fairness, ethics
In AI Ethics: Principles & Practice, which evidence best supports a rights result produced with Fairness?
Instructional figureDecision matrix
Stakeholders: from context to evidence
Stakeholders connects affected people and context to a documented mitigation in AI Ethics: Principles & Practice.
1Affected people and context
Define the purpose, intended user and AI Ethics 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
Bias: Evidence
Apply Bias through bias, measurement, impact.
Lesson
Key terms
Revision question
Bias with Accountability
bias, Accountability, ethics
In AI Ethics: Principles & Practice, which evidence best supports a bias result produced with Accountability?
Measurement with Transparency
measurement, Transparency, ethics
In AI Ethics: Principles & Practice, which evidence best supports a measurement result produced with Transparency?
Impact with AI Ethics
impact, AI Ethics, ethics
In AI Ethics: Principles & Practice, which evidence best supports a impact result produced with AI Ethics?
Instructional figureDecision matrix
Bias: from context to evidence
Bias connects affected people and context to a documented mitigation in AI Ethics: Principles & Practice.
1Affected people and context
Define the purpose, intended user and Accountability 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
Fairness: Governance
Apply Fairness through accountability, documentation, oversight.
Lesson
Key terms
Revision question
Accountability with Bias
accountability, Bias, ethics
In AI Ethics: Principles & Practice, which evidence best supports a accountability result produced with Bias?
Documentation with Fairness
documentation, Fairness, ethics
In AI Ethics: Principles & Practice, which evidence best supports a documentation result produced with Fairness?
Oversight with Accountability
oversight, Accountability, ethics
In AI Ethics: Principles & Practice, which evidence best supports a oversight result produced with Accountability?
Instructional figureProcess flow
Accountability: from context to evidence
Accountability connects affected people and context to a documented mitigation in AI Ethics: Principles & Practice.
1Affected people and context
Define the purpose, intended user and Bias 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
Accountability: Practice
Apply Accountability through risk review, participation, monitoring.
Lesson
Key terms
Revision question
Risk review with Transparency
risk review, Transparency, ethics
In AI Ethics: Principles & Practice, which evidence best supports a risk review result produced with Transparency?
Participation with AI Ethics
participation, AI Ethics, ethics
In AI Ethics: Principles & Practice, which evidence best supports a participation result produced with AI Ethics?
Monitoring with Bias
monitoring, Bias, ethics
In AI Ethics: Principles & Practice, which evidence best supports a monitoring result produced with Bias?
Instructional figureContinuous cycle
Risk Review: from context to evidence
Risk Review connects affected people and context to a documented mitigation in AI Ethics: Principles & Practice.
1Affected people and context
Define the purpose, intended user and Transparency 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 Ethics: Principles & Practice project using AI Ethics, Bias, Fairness.
Expected output: A working AI Ethics: Principles & Practice artefact plus an evidence-based self-review.
Tools: A suitable AI Ethics 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 Ethics.
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 Ethics scope.
Glossary
AI Ethics
A core concept or tool used in AI Ethics: Principles & Practice; its exact meaning is established in the relevant lesson.
Bias
A core concept or tool used in AI Ethics: Principles & Practice; its exact meaning is established in the relevant lesson.
Fairness
A core concept or tool used in AI Ethics: Principles & Practice; its exact meaning is established in the relevant lesson.
Accountability
A core concept or tool used in AI Ethics: Principles & Practice; its exact meaning is established in the relevant lesson.
Transparency
A core concept or tool used in AI Ethics: Principles & Practice; 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.