Artificial Intelligence (OCR A-Level Computer Science): Flashcards
Practise the cards
15 cards from this deck
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Practise the cards
15 cards from this deck
Definition of Artificial Intelligence (AI)
Definition of Artificial Intelligence (AI)
Computer systems performing tasks requiring human intelligence
Moral issues in AI - definition
Moral issues in AI - definition
Concerns about right/wrong in AI design and use
Moral issue example: accountability in AI
Moral issue example: accountability in AI
Who is responsible if AI causes harm (e.g., car accidents)?
Social issues in AI - definition
Social issues in AI - definition
Impact of AI on society and social structures
Social issue: job displacement by AI
Social issue: job displacement by AI
Automation replaces human workers, causing unemployment
Ethical issues in AI - definition
Ethical issues in AI - definition
Principles governing fair and just use of AI
Ethical issue: algorithmic bias in AI
Ethical issue: algorithmic bias in AI
AI inherits biases from training data, causing discrimination
Ethical issue: transparency in AI
Ethical issue: transparency in AI
AI decisions should be explainable, especially in high-stakes areas
Cultural issues in AI - definition
Cultural issues in AI - definition
How AI affects/is influenced by cultural norms and practices
Cultural issue: globalisation of AI
Cultural issue: globalisation of AI
AI from one culture may not align with another's values/norms
AI opportunity: healthcare advances
AI opportunity: healthcare advances
Early diagnosis, personalised treatment, drug discovery
AI opportunity: improved efficiency
AI opportunity: improved efficiency
AI performs tasks faster and more accurately than humans
AI risk: job loss and economic disruption
AI risk: job loss and economic disruption
Unemployment in manufacturing, retail, and transport sectors
AI risk: bias and discrimination
AI risk: bias and discrimination
AI can perpetuate or amplify societal biases
Common mistake: assuming AI is neutral
Common mistake: assuming AI is neutral
AI systems inherit biases from training data and developers
