The Impact of Artificial Intelligence
Passage A – AI in Personal Life
Artificial Intelligence (AI) has become deeply embedded in daily routines. From voice assistants that manage schedules to recommendation algorithms that personalize content, AI enhances convenience and efficiency. These systems learn from user behavior, adapting over time to provide increasingly tailored experiences.
Despite its benefits, AI raises ethical concerns. Data privacy, algorithmic bias, and over-reliance on automation are debated issues. Critics argue that users often lack awareness of how their data is used or how decisions are made. Transparency and digital literacy are essential to ensure responsible use of AI technologies.
Passage B – AI in Public Systems
Governments and institutions are integrating AI into public services. In healthcare, AI assists in diagnostics and patient monitoring. In transport, it optimizes traffic flow and predicts maintenance needs. These applications promise improved efficiency and resource management.
However, challenges persist. Bias in training data can lead to unfair outcomes, especially in areas like criminal justice or hiring. Public trust depends on accountability and oversight. Policymakers must balance innovation with ethical safeguards to ensure AI serves all citizens equitably.
📝 Questions 1-12
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📚 Answer Explanations
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- Q1: B – Passage A states that AI “learns from user behavior” and “provides increasingly tailored experiences.”
- Q2: False – The passage discusses “ethical concerns” including “data privacy, algorithmic bias.”
- Q3: AI systems learn from user behavior and adapt over time.
- Q4: Critics argue that users lack awareness of how AI decisions are made.
- Q5: Healthcare – Passage B says “In healthcare, AI assists in diagnostics and patient monitoring.”
- Q6: Traffic optimization and Patient monitoring are explicitly mentioned in Passage B.
- Q7: Bias – The passage warns that “bias in training data can lead to unfair outcomes.”
- Q8: Policymakers must balance innovation with ethical safeguards.
- Q9: AI predicts maintenance needs in transport systems.
- Q10: Prediction bridges the gap between algorithm and personalized output.
- Q11: Public trust depends on accountability and oversight.
- Q12: Both – Passage A discusses “algorithmic bias” in personal systems, and Passage B addresses bias in public systems.