Embed ethics across the AI lifecycle through a stage gate rather than a final review
AI for Business Institute · D8
Ethics & Compliance
Domain 8 of the AI for Business Institute practitioner standard. Embed ethics across the AI lifecycle through a stage gate rather than a final review; determine regulatory obligations by classifying each system on intended use; detect bias and understand why removing a protected field does not remove it; give affected people plain-language explanations with a route to human review; and check the licence, IP and provenance of training data before use.
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Time3.4 hours
Structure5 modules · 20 lessons
What you get
$250, and exactly what that buys.
- 5 modules, 20 lessons
One module per framework criterion, about 3.4 hours of material.
- Applied practice, not just reading
Applied scenarios, Branching decisions, Card sorting and more.
- Work at your own pace
Progress is saved as you go, so you can stop and resume on any device.
- Access that does not expire
Once bought, the course stays in your account. There is no renewal to keep it.
- Counts toward the full programme
Complete all nine domains and the Institute issues your programme completion certificate.
This does not include the accreditation exam. This course builds and evidences capability. Accreditation is a separate assessment, available with the full programme for $2,000, and the credential is awarded only if you pass it.
Why this course
Know what good looks like. Practise the decisions that make it real.
Domain 8 of the AI for Business Institute practitioner standard. Embed ethics across the AI lifecycle through a stage gate rather than a final review; determine regulatory obligations by classifying each system on intended use; detect bias and understand why removing a protected field does not remove it; give affected people plain-language explanations with a route to human review; and check the licence, IP and provenance of training data before use.
Capabilities
What you'll be able to do
determine regulatory obligations by classifying each system on intended use
detect bias and understand why removing a protected field does not remove it
give affected people plain-language explanations with a route to human review
and check the licence, IP and provenance of training data before use.
How you will learn
Learn. Practice. Prove.
Learn
Short, evidence-led lessons establish the standard and the reasoning behind it.
Practice
Interactive decisions, sorting and scenarios turn concepts into applied judgement.
Prove
A cumulative final exam checks mastery, followed by a spoken capstone.
Inside this courseApplied scenariosBranching decisionsCard sortingMatching exercisesPriority rankingConfidence checks
Curriculum
A concise view of the complete course.
Ethics embedded in the lifecycle
Why ethics must be built into the AI lifecycle rather than appended at the end, how an ethics stage gate embeds it, and why the point of greatest leverage is use case selection while the design is still open.
Regulatory compliance and AI system risk classification
How to determine which regulatory obligations apply to an AI system, why classification turns on intended use rather than technology, and what actually evidences a claim of regulatory compliance.
Bias, fairness and harm assessment
How to recognise algorithmic bias and where it comes from, why removing a protected characteristic does not remove the bias, and how to respond when a pre-deployment assessment finds a disparity.
Transparency, explainability and disclosure
What an explanation of an AI decision must achieve for the person affected, why a plain-language account of the principal factors plus a route to human review is the standard, and the difference between transparency and explainability.
Training data provenance, IP and licensing
Why the licence and IP status of training data must be checked before use, what a legal review of data licences establishes, and how provenance governs whether outputs are lawful and protectable.
Ready when you are