AIS104 · Responsible AI: Bias, Transparency and Human Oversight
About Course
Put responsible AI into practice: detect and reduce bias, make AI use transparent to the people it affects, and design human oversight that actually works rather than rubber-stamping.
Who it is for
Product owners, HR and operations leaders, analysts and compliance staff who deploy or approve AI that affects customers or employees.
Prerequisites
Complete AIS101 and AIS102; the lab uses ready-made Python notebooks, so no prior coding is required.
What you will be able to do
- Identify sources of bias across data, model design and deployment
- Measure group fairness with metrics such as selection rate, demographic parity difference and equalized odds
- Produce transparency artifacts such as a model card and a plain-language AI notice
- Design human oversight with clear authority, information and safeguards against automation bias
Lessons
- Lesson 1 · Where bias comes from and why it matters
- Lesson 2 · Measuring and mitigating unfairness
- Lesson 3 · Transparency and meaningful human oversight
- Lesson 4 · Lab and assignment
Lab: Measure and mitigate bias in a loan pre-approval model
You train a simple pre-approval classifier in a Colab notebook, then use Fairlearn's MetricFrame to compare selection rates and error rates across groups. You apply a mitigation such as threshold optimization, compare accuracy and fairness trade-offs, and document the result in a model card.
Free tools: Google Colab (free), scikit-learn and Fairlearn (open source), and a public synthetic or census-style dataset
Assessed assignment
Write a responsible AI review for an AI system that makes or supports decisions about people, such as applicant screening, credit pre-approval or customer service prioritization. Include a bias analysis with at least two fairness metrics, the mitigation you recommend and its trade-offs, a model card, a plain-language notice for affected people and a human oversight design that names who can override the system and how often overrides are reviewed.
| Criterion | Weight | What excellent work looks like |
|---|---|---|
| Bias analysis | 30% | Sources of bias are identified and at least two metrics are computed, interpreted correctly and compared across groups. |
| Mitigation and trade-offs | 20% | The mitigation is appropriate and its effect on accuracy and fairness is shown honestly. |
| Transparency artifacts | 25% | The model card is complete and the notice is accurate, short and understandable by a non-expert. |
| Human oversight design | 25% | Oversight has real authority, the information needed to decide, and measures against automation bias. |
Portfolio piece
A responsible AI review with fairness analysis, model card, AI notice and human oversight design.
Next step
Continue with AIS105 · AI Governance Frameworks and Policies · 8 hours · $56.00
Course Content
AIS104 · Responsible AI: Bias, Transparency and Human Oversight (6 h)
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Lesson 1 · Where bias comes from and why it matters
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Lesson 2 · Measuring and mitigating unfairness
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Lesson 3 · Transparency and meaningful human oversight
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Lesson 4 · Lab and assignment
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AIS104 Assessment · Responsible AI: Bias, Transparency and Human Oversight