Ethical, legal, cultural and environmental impacts of computing

OCR J277 Paper 1 (Section 1.6) tests students on the wider impacts of technology. Questions often ask students to "discuss", "evaluate" or "give arguments for and against" — so you need balanced answers that acknowledge both benefits and drawbacks.

Ethical issues

Ethics concerns what is morally right or wrong. Computing raises many ethical questions:

AI bias

• AI systems learn from training data. If the data contains human biases (racial, gender, socioeconomic), the AI may reproduce and amplify those biases.
• Examples: facial recognition systems less accurate on darker skin tones; loan approval algorithms that disadvantage certain demographics; hiring algorithms filtering out certain groups.
• Benefit: AI can be faster and more consistent than humans. Harm: encoded bias can cause discrimination at scale without accountability.

Censorship vs freedom of information

• Governments and platforms can restrict access to information online (e.g. blocking websites, filtering content).
• Argument for censorship: protects children from harmful content; prevents radicalisation; stops misinformation.
• Argument against: restricts freedom of expression; can be used to silence political opposition; undermines democracy.

Surveillance

• Technology enables mass surveillance: CCTV with facial recognition, tracking phones, monitoring internet traffic.
• Benefit: catches criminals; reduces terrorism; improves public safety.
• Harm: invasion of privacy; data can be misused by governments or companies; chilling effect on free speech.

The digital divide

• Not everyone has equal access to technology (internet, devices, digital skills).
• Causes: income inequality, geographic location, age, disability, lack of digital literacy.
• Impact: those without access miss out on education, job opportunities, government services and healthcare information.
• Closing the divide: affordable devices, subsidised broadband, digital literacy training.

Legal issues

Key UK legislation

Law	Year	What it covers
Data Protection Act (DPA 2018) / GDPR	2018	How organisations collect, store and process personal data. 8 principles including: data minimisation, purpose limitation, accuracy, security.
Computer Misuse Act (CMA 1990)	1990	Criminalises unauthorised access to computer systems (hacking); creating or distributing malware; modifying data without permission.
Copyright, Designs and Patents Act (CDPA 1988)	1988	Protects creators' rights over software, music, images, text. Unauthorised copying is illegal.

Software licences

• Proprietary (closed source): code is private; users pay for a licence; cannot modify or redistribute.
• Open source: source code publicly available; can be used, modified and redistributed freely (subject to licence terms, e.g. GPL, MIT).

Cultural and environmental impacts

Cultural impacts

• Globalisation: technology connects people worldwide; cultures share ideas but local cultures may be eroded.
• Social media: changes how people communicate, consume news and form communities.
• Changing work patterns: remote working; gig economy (Uber, Deliveroo); automation displacing jobs.

Environmental impacts

• Data centres: use enormous amounts of electricity for servers and cooling (~1–2% of global electricity).
• E-waste: discarded electronics contain toxic materials (lead, mercury, cadmium). Improper disposal pollutes soil and water.
• Manufacturing: producing chips and devices uses rare earth metals and large amounts of water and energy.
• Circular economy: refurbishing, reusing and recycling devices; manufacturers designing for repairability (Right to Repair legislation).

Common OCR exam mistakes

1. Giving one-sided answers to "discuss" questions — OCR mark schemes award marks for balanced arguments.
2. Confusing the Data Protection Act with the Computer Misuse Act — DPA is about personal data; CMA is about unauthorised access/hacking.
3. Forgetting the Digital Divide is an ethical issue — it is about inequality of access, not just a technological fact.
4. Saying AI bias "can be fixed by using more data" — more data may help but can also introduce more bias if data is still unrepresentative.