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Artificial intelligence, machine learning and robotics
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AI now helps decide what we watch, who gets a loan and how cars drive. That raises big questions about fairness and responsibility.
Key terms
Artificial intelligence (AI): computer systems that do tasks that normally need human intelligence, such as recognising speech or images.
Machine learning: a type of AI where a system learns patterns from large amounts of data, rather than being given every rule by a programmer.
Robotics: machines that sense their surroundings and carry out physical tasks.
Machine learning: a type of AI where a system learns patterns from large amounts of data, rather than being given every rule by a programmer.
Robotics: machines that sense their surroundings and carry out physical tasks.
Ethical and legal issues
Accountability: who is responsible when an AI makes a bad decision?
Safety: systems such as self-driving cars and robots must not put people at risk.
Algorithmic bias: if the training data is biased, the system's decisions can be unfair, for example favouring one group of job applicants.
Legal liability: if harm is caused, is the manufacturer, the programmer or the owner legally to blame?
Safety: systems such as self-driving cars and robots must not put people at risk.
Algorithmic bias: if the training data is biased, the system's decisions can be unfair, for example favouring one group of job applicants.
Legal liability: if harm is caused, is the manufacturer, the programmer or the owner legally to blame?
Reducing the risks
Train systems on varied, representative data and test them for bias.
Keep humans involved in important decisions and make it clear how decisions are made.
Set laws and safety standards before systems are widely used.
Keep humans involved in important decisions and make it clear how decisions are made.
Set laws and safety standards before systems are widely used.
A company uses AI to shortlist job applicants. It was trained on past hires, who were mostly men. What problem might occur?
- The AI learns patterns from its training data.
- If past hires were mostly men, it may unfairly favour male applicants.
Answer: Algorithmic bias: unfair decisions against women applicants
AI performs tasks needing human-like intelligence; machine learning learns from data. Issues: accountability, safety, algorithmic bias from biased data, and legal liability when harm occurs.
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