A4.4 Ethical considerations: notes and practice
A4.4 covers the ethics of machine learning and computing: bias, fairness, explainability, accountability, privacy and consent, intellectual property and the effects on society.
Paper 1SL and HL2 practice questions
What you need to know
Our summary of the topic in our own words, not the IB guide.
- Explain how bias enters a machine learning system through data, labels or design choices
- Distinguish accuracy from fairness and explain why a highly accurate model can still be unfair
- Explain why some models are hard to explain and what a counterfactual explanation offers
- Analyse a described deployment and identify who is affected and how harm could occur
- Discuss accountability when an automated decision causes harm
- Explain what personal and sensitive personal data are, and what informed consent means
- Describe the intellectual property questions raised by training models on existing work
- Evaluate the social effects of embedding computing in everyday life, including surveillance, automation and dependence
- Construct a balanced argument for and against a described use of a computing system
Practice questions
Original questions written for the 2027 course, marked as you go. Easiest first.
- Bias in a reading support modelWritten · Stretch
- Fairness of a bike-share model in outer districtsWritten · Stretch
Exam tips for A4.4
- Build balanced answers: give an argument on each side and finish with a reasoned judgement.
- Point to where bias came from in the scenario (the data, the labels or the design).
- Use the people in the scenario. Say who is affected and how.
Updated 24 October 2026. Topic codes are used as labels. Everything on this page is written by ExamSpine and is not endorsed by the IB.