AI image recognition - training images

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About this lesson

These images are to be used to complete the DT Hub lesson: AI image recognition: exploring limitations and bias.

Year band: 5-6, 7-8

Curriculum Links Assessment

Curriculum Links

Links with the Digital Technologies Curriculum Area

Strand	Year	Content Description
Processes and Production Skills	Years 5–6	Acquire, store and validate different types of data, and use a range of software to interpret and visualise data to create information (ACTDIP016) Define problems in terms of data and functional requirements drawing on previously solved problems (ACTDIP017) Implement digital solutions as simple visual programs involving branching, iteration (repetition), and user input (ACTDIP020) Explain how student solutions and existing information systems are sustainable and meet current and future local community needs (ACTDIP021)
Processes and Production Skills	Years 7–8	Analyse and visualise data using a range of software to create information, and use structured data to model objects or events (ACTDIP026) Define and decompose real-world problems taking into account functional requirements and economic, environmental, social, technical and usability constraints (ACTDIP027) Implement and modify programs with user interfaces involving branching, iteration and functions in a general-purpose programming language (ACTDIP030) Evaluate how student solutions and existing information systems meet needs, are innovative, and take account of future risks and sustainability (ACTDIP031)

Strand

Year

Content Description

Processes and Production Skills

Years 5–6

Acquire, store and validate different types of data, and use a range of software to interpret and visualise data to create information (ACTDIP016)

Define problems in terms of data and functional requirements drawing on previously solved problems (ACTDIP017)

Implement digital solutions as simple visual programs involving branching, iteration (repetition), and user input (ACTDIP020)

Explain how student solutions and existing information systems are sustainable and meet current and future local community needs (ACTDIP021)

Processes and Production Skills

Years 7–8

Analyse and visualise data using a range of software to create information, and use structured data to model objects or events (ACTDIP026)

Define and decompose real-world problems taking into account functional requirements and economic, environmental, social, technical and usability constraints (ACTDIP027)

Implement and modify programs with user interfaces involving branching, iteration and functions in a general-purpose programming language (ACTDIP030)

Evaluate how student solutions and existing information systems meet needs, are innovative, and take account of future risks and sustainability (ACTDIP031)

ICT Capability

Typically, by the end of Year 8, students:

Identify the impacts of ICT in society (Applying social and ethical protocols and practices when using ICT)

explain the benefits and risks of the use of ICT for particular people in work and home environments

Select and evaluate data and information (Investigating with ICT)

assess the suitability of data or information using appropriate own criteria

Assessment

Teacher assessment

Using original photographs created by students, or content sourced online, students work in pairs to create a new AI models for one of the example topics below:

Distinguish different sign language hand postures
Distinguish different semaphore poses
Distinguish favourite sport team based on face paint
A topic chosen by a student or students.

Choose from the following suggested assessment approaches and task that will best suit your students.

Possible tasks	Relevant content description(s)
Run a two-part investigation by training the model using a set of images that is ‘biased’ or skewed in some way before the first test, then retraining the model using a set of images that is ‘unbiased’ (for the purposes of the investigation) before the second test. Write a report, detailing: biased image set unbiased image set testing image set results of both tests, including decisions and confidence figures an evaluation discussing any difference in results between the biased and unbiased models.	ACTDIP016/ ACTDIP026 ACTDIP017/ ACTDIP027 ACTDIP021/ ACTDIP031
Design and develop a Scratch or Python program to simulate a computer application that uses your AI model. Create a ‘quick start’ guide or video to demonstrate and explain the program. IMPORTANT NOTE: It is difficult for students to share finished programs from Machine Learning for Kids. See the note SAVING PROGRAMS in the Testing the AI section.	ACTDIP016/ ACTDIP026 ACTDIP017/ ACTDIP027 ACTDIP020 (Scratch) / ACTDIP030 (Python) ACTDIP021/ ACTDIP031
Write an essay, with reference to real examples, about what software developers could do to avoid or reduce algorithmic bias in the future.	ACTDIP016/ ACTDIP026 ACTDIP017/ ACTDIP027 ACTDIP021/ ACTDIP031

Possible tasks

Relevant content description(s)

Run a two-part investigation by training the model using a set of images that is ‘biased’ or skewed in some way before the first test, then retraining the model using a set of images that is ‘unbiased’ (for the purposes of the investigation) before the second test.

Write a report, detailing:

biased image set
unbiased image set
testing image set
results of both tests, including decisions and confidence figures
an evaluation discussing any difference in results between the biased and unbiased models.

ACTDIP016/ ACTDIP026

ACTDIP017/ ACTDIP027

ACTDIP021/ ACTDIP031

Design and develop a Scratch or Python program to simulate a computer application that uses your AI model.

Create a ‘quick start’ guide or video to demonstrate and explain the program.

IMPORTANT NOTE: It is difficult for students to share finished programs from Machine Learning for Kids. See the note SAVING PROGRAMS in the Testing the AI section.

ACTDIP016/ ACTDIP026

ACTDIP017/ ACTDIP027

ACTDIP020 (Scratch) / ACTDIP030 (Python)

ACTDIP021/ ACTDIP031

Write an essay, with reference to real examples, about what software developers could do to avoid or reduce algorithmic bias in the future.

ACTDIP016/ ACTDIP026

ACTDIP017/ ACTDIP027

ACTDIP021/ ACTDIP031

Face gallery

You will notice that the sizes and cropping of the images vary. This has been done deliberately to model a real-world situation where faces are being recognised, as someone may be closer to/ farther from the camera, standing to the side etc. In these cases, AI must work harder as the pictures are not all uniform/ in the same place.

Blonde-haired man with glasse