Understanding bias in AI
Lesson details
Learning outcome
I can explain how data patterns lead to AI bias and describe why outputs may be unfair or unrepresentative.
Key learning points
- AI bias occurs when a system produces inaccurate results because it has learnt from training data that is flawed.
- Because AI systems learn patterns from human-created data, they can repeat and increase existing social stereotypes.
- AI tools trained on datasets where certain groups are missing or under-represented lead to tools that perform poorly.
- Critically evaluating AI outputs is essential for identifying bias.
Keywords
Bias - when something is unfair towards or against something or someone
Dataset - a collection of information used to train an AI system, such as images, text or numbers
Data bias - when data is unfair or unbalanced, which can lead to wrong or unfair results
Fairness - when all groups of people are treated equally and there are no outcomes that disadvantage or misrepresent some people more than others
Common misconception
AI systems are naturally neutral and objective because they are machines.
An AI system will replicate and amplify any human prejudices present in its training data, and its training data may be inadequate for the task it is being used for.
Teacher tip
Task A and Task C could be completed as a card sort activity with students working idependently or as part of a group.
Licence
Lesson video
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Prior knowledge starter quiz
6 Questions
Q1.Why is it important to remember that a chatbot is not human?
Q2.Which example best shows healthy use of a chatbot?
Q3.Why can anthropomorphism be risky when using chatbots?
Q4.Which situation shows emotional reliance on a chatbot?
Q5.What is a key privacy risk when using chatbots?
Q6.Which statement best reflects responsible digital boundaries?
Assessment exit quiz
6 Questions
Q1.Which statement best explains why AI systems are not naturally neutral or objective?
Q2.If an AI system is trained with images of green apples and red tomatoes, why might the system wrongly label a red apple as a tomato?
Q3.Which of the following scenarios is most likely an example of representation bias?
Q4.A predictive policing system sends more patrols to one postcode because past arrest data showed higher crime there. Why is this a concern?
Q5.Which questions would help you decide whether an AI output might be biased?
Q6.Which actions can help reduce bias in AI systems?
To help you plan your 8 digital literacy lesson on: Understanding bias in AI, download all teaching resources for free and adapt to suit your pupils' needs...
To help you plan your 8 digital literacy lesson on: Understanding bias in AI, download all teaching resources for free and adapt to suit your pupils' needs.
The starter quiz will activate and check your pupils' prior knowledge, with versions available both with and without answers in PDF format.
We use learning cycles to break down learning into key concepts or ideas linked to the learning outcome. Each learning cycle features explanations with checks for understanding and practice tasks with feedback. All of this is found in our slide decks, ready for you to download and edit. The practice tasks are also available as printable worksheets and some lessons have additional materials with extra material you might need for teaching the lesson.
The assessment exit quiz will test your pupils' understanding of the key learning points.
Our video is a tool for planning, showing how other teachers might teach the lesson, offering helpful tips, modelled explanations and inspiration for your own delivery in the classroom. Plus, you can set it as homework or revision for pupils and keep their learning on track by sharing an online pupil version of this lesson.
Explore more key stage 3 digital literacy lessons from the Using AI and digital tools responsibly unit, dive into the full secondary digital literacy curriculum, or learn more about lesson planning.