This key facts covers Key Ethical Issues within Ethical Issues for GCSE Computer Science. Revise Ethical Issues in Impacts of Technology for GCSE Computer Science with 15 exam-style questions and 12 flashcards. This topic appears regularly enough that it should still be part of a steady revision cycle. It is section 2 of 5 in this topic. Use this key facts to connect the idea to the wider topic before moving on to questions and flashcards.
Topic position
Section 2 of 5
Practice
15 questions
Recall
12 flashcards
Key Ethical Issues
1. Privacy
The right to control personal information. Concerns include social media tracking, facial recognition, and data selling.
- Social media platforms tracking browsing habits
- Facial recognition in public spaces
- Companies selling user data to third parties
- Location tracking through mobile devices
2. Surveillance
Monitoring people's activities raises questions about freedom vs security.
- CCTV cameras in public spaces
- Workplace monitoring of employees
- Government surveillance programs
- Smart home devices recording conversations
3. AI Decision Making
Algorithms making important decisions about people's lives.
- Credit scores determined by AI
- Job screening and hiring automation
- Criminal sentencing recommendations
- Insurance premiums based on data analysis
4. Digital Divide
The gap between those with and without technology access creates inequality.
- Rural areas with poor broadband access
- Developing countries lacking infrastructure
- Elderly people struggling with digital services
- Low-income groups unable to afford devices
5. Automation and Jobs
Machines replacing human workers raises questions about employment and economic fairness.
- Self-checkout replacing cashiers
- Autonomous vehicles threatening driver jobs
- Chatbots replacing customer service roles
- Factory automation reducing manufacturing jobs
6. Bias in AI
Algorithms reflecting societal prejudices from training data.
- Facial recognition less accurate for certain ethnicities
- Biased training data leading to discriminatory outcomes
- Gender bias in job recommendation systems
- Socioeconomic bias in credit scoring