Automated and Emerging Technologies · 4 question types
Past paper frequency (2018 to 2024)
This topic accounts for approximately 4% of your exam marks.
AI applications and machine learning concepts are growing in exam prominence.
Across all AI systems, similar trade-offs apply.
| Advantage | Why it matters |
|---|---|
| Increased efficiency | Tasks are completed faster than humans can manage |
| Increased accuracy | Well-trained AI systems are often more reliable than humans at narrow tasks (medical imaging, defect detection) |
| Scalability | One trained model can serve millions of users simultaneously, around the clock |
| Works 24/7 | No breaks, no holidays, no fatigue |
| Handles dangerous or boring tasks | Frees humans from work that is unsafe or repetitive |
| Personalisation | Adapts to each user's preferences and history |
| Disadvantage | Why it matters |
|---|---|
| Job losses | Tasks that AI can do may not need human workers, leading to unemployment in some sectors |
| Bias in decision-making | AI trained on biased data may make unfair decisions (hiring, lending, policing) |
| Loss of human skill | If AI takes over a task, humans may lose the skill to do it manually |
| Ethical concerns | Concerns about privacy, surveillance, autonomous weapons, deepfakes and manipulation |
| High setup and energy cost | Training large AI models is expensive and uses huge amounts of electricity |
| Lack of accountability | If an AI system causes harm, working out who is legally responsible can be very difficult |
| Dependence | As more decisions are delegated to AI, organisations and societies become reliant on systems they may not fully understand |
Three specific issues come up regularly:
These questions do not have single right answers; the syllabus expects you to be able to recognise them and discuss them at a basic level.