Robotics is the design and use of machines that sense their surroundings and carry out physical tasks. Robots are used in factories, warehouses, surgery and space exploration.

What is algorithmic bias?

Algorithmic bias is an automated system producing results that are unfair to certain groups of people. The bias often comes from the data the system learned from.

How can training data cause algorithmic bias?

A system trained mostly on data from one group may work less well for others. A face recognition system trained mainly on lighter-skinned faces can misidentify darker-skinned people.

Give an example of harm caused by algorithmic bias.

A recruitment system trained on past hiring decisions could favour the kinds of people hired before. Qualified applicants from other groups would then be rejected unfairly.

Edexcel GCSE Computer Science 1CP2

Computational Thinking

Topic overview Decomposition, abstraction and subprograms Algorithms, flowcharts and pseudocode Operators and trace tables Logic errors, test data and efficiency Searching and sorting algorithms Truth tables

Data

Topic overview Binary, states and unsigned integers Two's complement Binary addition, overflow and shifts Hexadecimal ASCII and the limits of binary representation Bitmap images Sound sampling Storage units, file sizes and compression

Computers

Topic overview Von Neumann architecture and the fetch-decode-execute cycle Secondary storage: magnetic, optical and solid state Embedded systems Operating systems Utility software and robust software Low-level and high-level languages and translators

Networks

Topic overview LANs, WANs and the structure of the internet Wired and wireless connectivity and network speed Network protocols The four-layer TCP/IP model Bus, star and mesh topologies Network security and protection

Issues and Impact

Topic overview Environmental impact of digital devices Personal data: privacy, ownership and consent Artificial intelligence, machine learning and robotics Intellectual property: copyright, patents, trademarks and licences Malware, hacking and social engineering Protecting digital systems and data

Problem Solving with Programming

Topic overview Readable code, debugging and testing Selection and loops in Python Data types, conversion, variables and constants String handling Lists, two-dimensional lists and records Keyboard input, screen output and CSV files Validation and authentication in Python Operators, functions and variable scope Turtle graphics

Issues and Impact

Artificial intelligence, machine learning and robotics

3 things to know
Paper 1

Overview

AI, machine learning and robotics, algorithmic bias and its causes, accountability, safety, legal liability for self-driving cars, and the effect on jobs.

What you need to know

Answer each one before you reveal it.

Your recallNot started

Key term

What is artificial intelligence?

Key term

What is machine learning?

Key term

What is robotics?

Questions from this subtopic

Each one with the model answer underneath.

1
What is artificial intelligence?
Model answer
Artificial intelligence is the ability of a computer system to carry out tasks that normally need human intelligence. Examples include recognising speech, translating text and making recommendations.
2
What is machine learning?
Model answer
Machine learning is a type of artificial intelligence in which a system improves at a task by finding patterns in data. The system learns from examples rather than being given every rule.
3
What is robotics?
Model answer
Robotics is the design and use of machines that sense their surroundings and carry out physical tasks. Robots are used in factories, warehouses, surgery and space exploration.
4
What is algorithmic bias?
Model answer
Algorithmic bias is an automated system producing results that are unfair to certain groups of people. The bias often comes from the data the system learned from.
5
How can training data cause algorithmic bias?
Model answer
A system trained mostly on data from one group may work less well for others. A face recognition system trained mainly on lighter-skinned faces can misidentify darker-skinned people.
6
Give an example of harm caused by algorithmic bias.
Model answer
A recruitment system trained on past hiring decisions could favour the kinds of people hired before. Qualified applicants from other groups would then be rejected unfairly.

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