5.2.2Artificial intelligence, machine learning and robotics
Edexcel GCSE Computer Science (1CP2) · Issues and impact › Ethical and legal issues
Practise Artificial intelligence, machine learning and robotics. 11 exam-style questions on this subtopic, at up to four difficulty levels, with full mark schemes and a progress tracker. Free, no account needed.
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Define the term 'artificial intelligence (AI)'.
Computer systems that perform tasks that normally need human intelligence.
Define the term 'robot'.
A computer-controlled machine that carries out physical tasks automatically.
Sample questions
Written for this site in the style of Edexcel exam questions. They are not taken from real past papers.
Question 1Easy3 marks
(a) Define the term 'artificial intelligence (AI)'.[1]
(b) Give two examples of tasks carried out by AI systems.[2]
Show the answer and mark scheme
(a)Answer: Computer systems that perform tasks that normally need human intelligence.
a computer system that can carry out tasks that normally need human intelligence, e.g. recognising speech or images, or making decisions
(b)Answer: Any two from: speech recognition, face recognition, translation, recommendations, fraud detection, self-driving, medical diagnosis.
recognising speech (voice assistants)
recognising faces or objects in images
translating languages
recommending films, music or products
detecting fraud in bank transactions
driving vehicles
helping to diagnose illnesses from medical scans
chatbots answering questions
filtering spam emails
Question 2Medium4 marks
(a) Define the term 'machine learning'.[2]
(b) Explain how the training data used by a machine learning system affects its accuracy.[2]
Show the answer and mark scheme
(a)Answer: A system learns patterns from training data, so it improves without being given explicit rules.
a type of AI in which a system learns patterns from (large amounts of) data
so it improves its performance or makes predictions without being explicitly programmed with rules for every situation
(b)Answer: It only learns from its examples, so poor or unrepresentative data leads to errors and bias.
the system can only learn from the examples it is given, so the training data needs to be large, accurate and representative of real situations
if the data is too small, incorrect or unrepresentative, the system makes more errors or is biased when it meets new data
Question 3Hard4 marks
An autonomous delivery robot travelling along a pavement collides with a pedestrian and injures them.
Explain why it can be difficult to decide who is legally responsible for the accident.[4]
Show the answer and mark scheme
Answer: Responsibility is spread across developers, manufacturer and operator; the robot decided for itself in a way that may not be explainable, and the law has not caught up.
many different parties could be responsible: the programmers, the manufacturer, the company operating the robot or the people who maintain it
the robot made the decision itself, with no human controlling it at the time
if it uses machine learning, its behaviour came from its training data, so no one directly programmed that action
it may be impossible to explain exactly why it made the decision (a 'black box')
laws were written for human drivers and operators and may not clearly cover autonomous machines
the pedestrian's own actions or unexpected conditions may also have played a part