Ethical Issues in Computing: KS3 Guide

Computing raises important ethical questions about privacy, fairness, and responsibility that affect all of us. At KS3, students are expected to reflect on how technology impacts society — not just how it works technically. From digital footprints to AI bias and cybercrime, thinking carefully about the ethics of computing is now an examinable part of the curriculum.

Why do ethical issues matter in computing?

Technology is not neutral. Every piece of software embodies choices about what data to collect, who can access it, and whose interests it serves. Those choices have real consequences for real people.

Ethics is the branch of philosophy concerned with what is right, fair, and just. Computing ethics asks questions like: Is it right for a social media company to track your location without clear consent? Should an algorithm decide who gets a job interview? Who is responsible when an autonomous vehicle causes an accident?

At KS3, you do not need to resolve these questions definitively — examiners want to see that you can identify the ethical issue, consider multiple perspectives, and form a reasoned view. The DfE computing programme of study explicitly requires students to "evaluate the impacts of digital technology on individuals and society" (gov.uk/government/publications/national-curriculum-in-england-computing-programmes-of-study).

What is a digital footprint?

Every time you use the internet — clicking a link, logging into an app, watching a video, or sending a message — you leave behind a trail of data. Collectively, this is your digital footprint.

There are two kinds:

Active footprint — data you knowingly provide: social media posts, profile information, emails, comments.

Passive footprint — data collected without you actively providing it: browsing history, location data, cookies tracking which products you viewed, timestamps on messages.

Your digital footprint can persist for years, be shared with advertisers, accessed by employers, or — if security fails — exposed in a data breach. Thinking before you post and being selective about which apps you grant permissions to are basic steps toward managing your footprint responsibly.

What are the privacy concerns with data collection?

Companies collect vast amounts of personal data. The General Data Protection Regulation (GDPR), which became UK law through the UK GDPR after Brexit, gives individuals rights over their personal data including the right to:

Know what data is held about them

Request deletion ("right to be forgotten")

Correct inaccurate data

Object to automated decision-making

Type of data collected | Who collects it | Why
Search history | Search engines (Google, Bing) | Targeted advertising
Location data | Mobile apps, navigation services | Personalisation, advertising
Purchasing behaviour | Retailers, banks | Recommendations, fraud detection
Social connections | Social media platforms | Network analysis, advertising
Health data | Fitness apps, NHS records | Health services, research

The privacy concern is not only about what data is collected, but about whether individuals genuinely understand and consent to that collection, and whether organisations store it securely. Data breaches — where personal data is leaked or stolen — affect millions of people every year.

What are the ethical issues with artificial intelligence?

AI systems make decisions that affect people's lives, yet those decisions can be difficult to understand or challenge. Key concerns include:

Algorithmic bias — an AI trained on historical data can perpetuate historical prejudices. If past hiring data reflects bias against certain groups, an AI trained on that data may reproduce the same bias in future decisions. This has been observed in recruitment, credit scoring, and even healthcare.

Transparency and explainability — when an AI denies a loan application or flags a security threat, can it explain why in terms a human can understand and challenge? Many modern AI systems (particularly deep learning networks) are "black boxes" — even their designers cannot fully explain individual decisions.

Surveillance — facial recognition technology can identify individuals in a crowd without their consent. Its use by law enforcement raises serious questions about privacy, civil liberties, and the potential for authoritarian misuse.

Job displacement — automation powered by AI is changing the nature of work, making some jobs redundant while creating others. The ethical question is how society should manage this transition fairly.

What is cybercrime and who is responsible?

Cybercrime is any criminal activity that involves a computer, network, or digital device. Examples include:

Hacking — gaining unauthorised access to a computer system

Malware — distributing viruses, ransomware, or spyware

Phishing — using deceptive emails or websites to steal credentials

Identity theft — using someone else's personal information fraudulently

Cyberbullying — using digital means to harass or harm others

In the UK, key legislation includes:

Computer Misuse Act 1990 — makes it illegal to access a computer without authorisation, to access a computer with intent to commit further offences, or to make unauthorised modifications to data. Penalties include prison sentences.

Data Protection Act 2018 (incorporating UK GDPR) — regulates how personal data is stored and used.

Responsibility for cybercrime is not always simple. A company that fails to implement basic security practices and suffers a breach that exposes customer data bears some responsibility, even if the direct perpetrator was an external attacker.

How can you be a responsible digital citizen?

Being a responsible digital citizen means making thoughtful choices about how you use technology and how you treat others online:

Protect your data — use strong, unique passwords; enable two-factor authentication; read privacy settings before accepting them.

Think before you post — consider whether something you post could harm yourself or others, and whether you would say it face-to-face.

Respect intellectual property — do not download, copy, or share content without the right to do so.

Question what you read — consider the source, look for evidence, and be sceptical of content designed to provoke a strong emotional reaction.

Report harm — if you witness cyberbullying or encounter harmful content, report it to the platform and, if serious, to an adult or the police.

Frequently asked questions

What are ethical issues in computing at KS3?

Ethical issues in computing are questions about what is right, fair, or responsible in the development and use of technology. At KS3 the main areas are: privacy and data collection, digital footprints, algorithmic bias, cybercrime and the law, and responsible use of AI. Exam questions typically ask you to identify an ethical issue in a given scenario and explain arguments on both sides.

What is a digital footprint and why does it matter?

A digital footprint is the trail of data created by your online activity — both data you actively provide (posts, logins) and data passively collected about you (browsing history, location). It matters because it persists, can be shared with advertisers or future employers, and can be exposed in data breaches. Being aware of your digital footprint allows you to make more informed choices about your online behaviour.

What law covers hacking in the UK?

The Computer Misuse Act 1990 is the primary legislation. It makes three activities criminal offences: (1) unauthorised access to a computer, (2) unauthorised access with intent to commit a further offence (e.g. fraud), and (3) unauthorised modification of computer material. Even accessing a computer system you are not supposed to — even without causing any damage — is a criminal offence under this Act.

What is algorithmic bias and why is it a problem?

Algorithmic bias occurs when an AI or automated system produces systematically unfair outcomes for certain groups. It usually arises because the data used to train the algorithm reflects existing societal biases, or because the algorithm optimises a metric that inadvertently disadvantages certain groups. It is a problem because automated decisions — in hiring, lending, healthcare, and criminal justice — can affect people's lives significantly, and biased algorithms can perpetuate or worsen existing inequalities at scale.

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