Artificial Intelligence

Automated and Emerging Technologies · 4 question types

Exam Frequency Analysis

Past paper frequency (2018 to 2024)

This topic accounts for approximately 4% of your exam marks.

increasing

Rare

Increasing4%

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:

Algorithmic bias: AI trained on data that reflects historical bias can reproduce or amplify it. A hiring AI trained on past data may unfairly favour the demographics historically hired.
Privacy: AI systems often rely on huge amounts of personal data. How that data is collected, stored and shared raises real concerns.
Accountability: when an autonomous AI system causes harm, who is responsible: the developer, the operator, the company, or the user? Legal frameworks are still catching up.

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.