What ethical and legal issues arise from AI, machine learning and robotics, and how is intellectual property protected?
Understand ethical and legal issues associated with artificial intelligence, machine learning and robotics (accountability, safety, algorithmic bias, legal liability), and methods of intellectual property protection (copyright, patents, trademarks, licencing).
A focused answer to Edexcel GCSE Computer Science 5.2.2 and 5.2.3, covering the ethical and legal issues of AI, machine learning and robotics (accountability, safety, algorithmic bias, legal liability) and intellectual property protection (copyright, patents, trademarks, licensing).
Reviewed by: AI editorial process; not yet individually human-reviewed
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What this dot point is asking
Edexcel wants you to explain the ethical and legal issues of artificial intelligence (AI), machine learning and robotics (accountability, safety, algorithmic bias, legal liability), and the methods of intellectual property protection (copyright, patents, trademarks, licensing).
AI, machine learning and robotics: the ethical and legal issues
The issues Edexcel names should be addressed directly:
- Safety. AI and robots make decisions that can affect physical safety (a self-driving car, a medical diagnosis system, a factory robot). A fault or wrong decision could cause injury or harm, so reliability, testing and human oversight are critical.
- Accountability. When an AI system makes a harmful or wrong decision, it can be hard to say who is accountable, because the system may have "learned" its behaviour and no single person decided the outcome.
- Algorithmic bias. Machine learning learns from data, so if the training data is unrepresentative or biased, the system can treat some groups unfairly (for example a recruitment AI favouring one type of applicant, or facial recognition working less well for some people). This is both unethical and potentially unlawful (discrimination).
- Legal liability. Linked to accountability, this is the legal question of who is responsible in law when an AI or robot causes harm: the owner, the manufacturer, or the software developer. The law is still catching up, which makes liability uncertain.
Why these issues are hard
The difficulty is that AI systems can be complex and opaque: a machine-learning model's decisions emerge from patterns in data, not from rules a person wrote, so explaining why it did something, and who is to blame if it is wrong, is genuinely hard. Algorithmic bias is especially important because it can be hidden: the system may appear neutral while systematically disadvantaging some people because of the data it learned from. These are why clear rules on accountability, careful choice of training data, testing and human oversight are needed.
Intellectual property protection
Each protects a different kind of creation. Copyright is the main one for software and content and is automatic. Patents protect technical inventions and must be applied for. Trademarks protect the brand (the name and logo). Licensing is how an owner permits use on set terms, for example a software licence stating how many devices it can be installed on and whether it may be copied. Together they let creators control and benefit from their work.
Try this
Q1. State what is meant by algorithmic bias. [1 mark]
- Cue. When an AI system, trained on unrepresentative or biased data, treats some people or groups unfairly.
Q2. State which IP protection automatically covers an original piece of software. [1 mark]
- Cue. Copyright.
Exam-style practice questions
Practice questions written in the style of Pearson Edexcel exam questions on this dot point, with worked answer explainers. The year tag is the paper they imitate, not the source.
Edexcel 20236 marksSelf-driving cars use artificial intelligence to make decisions. Discuss the ethical and legal issues associated with using artificial intelligence in self-driving cars.Show worked answer →
A "Discuss" answer should explore several issues and reach a judgement.
Safety: the AI must drive safely in unpredictable conditions; a fault or wrong decision could cause injury or death, so reliability and testing are critical.
Accountability and legal liability: if a self-driving car causes a crash, it is unclear who is responsible, the owner, the manufacturer or the software developer, raising hard questions of legal liability and who should be held accountable.
Algorithmic bias: if the AI was trained on data that does not represent all situations or people, it may behave less safely in some cases (for example failing to recognise certain pedestrians), which is unfair and dangerous.
Decision-making ethics: the car may face situations where any action causes harm, and how it is programmed to choose raises ethical questions.
A balanced conclusion weighs the potential benefits (fewer accidents from human error, mobility for those who cannot drive) against the safety risks and the unresolved questions of liability and bias, concluding that clear rules on responsibility and rigorous testing are needed.
Markers reward exploring several issues (safety, accountability and liability, algorithmic bias, decision ethics), developed points and a balanced judgement.
Edexcel 20224 marksState what is meant by copyright and explain how software licensing allows a company to control the use of its software.Show worked answer →
Copyright is the automatic legal protection that gives the creator of an original work (such as software, music or writing) the exclusive right to copy, distribute and use it, so others cannot do so without permission.
A software licence is a legal agreement that sets out how the software may be used, for example how many devices it can be installed on, whether it may be copied or modified, and for how long. It lets the company control and limit use (and charge for it) while keeping ownership, so users must agree to the licence terms to use the software legally.
Markers reward defining copyright (exclusive right over an original work) and explaining licensing (an agreement setting out permitted use, letting the owner control how the software is used while retaining ownership).
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Sources & how we know this
- Pearson Edexcel GCSE (9-1) Computer Science (1CP2) specification — Pearson (2020)