Artificial intelligence
What is artificial intelligence?
Artificial intelligence (AI) is the ability of machines to mimic human capabilities in a way that we would consider 'smart'.
You most likely have come across – or are aware of – AI applications such as self-driving cars, facial recognition, chess or go players, security systems, or speech/voice recognition (for example, those used in an intelligent virtual assistant).
How is AI different from 'normal' computing?
In conventional computing, a programmer writes a computer program that precisely instructs a computer what to do to solve a particular problem. With AI, however, the programmer instead writes a program that allows the computer to learn to solve a problem by itself.
That sounds like overdoing it, but this is really the way we do things. At school, students learn the rules that allow them to solve a vast number of different problems. Instead of teaching 1,000 solutions to 1,000 problems, teachers teach students the practices and techniques of how to solve a variety of problem instead. The idea behind AI is that we teach a computer to learn to solve problems. And because machines are good at crunching large amounts of data without ever getting tired, computers can solve some tough problems that our brains would struggle with.
What is machine learning?
Machine Learning is an application of AI. Over recent times, the increased amount of data available for use in powerful computer systems has enabled the implementation of AI. With Machine Learning, we give the machine lots of examples of data, demonstrating what we would like it to do so that it can figure out how to achieve a goal on its own. The machine learns and adapts its strategy to achieve this goal.
Artificial Neural Networks (ANNs - some of which are called Deep Learning) are inspired by the workings of the biological brain's neural networks. ANNs can learn to identify patterns by using a feedback loop to learn from mistakes and improve their results.
Is there an AI that can solve any problem?
Current AI solutions are limited to particular applications, such as Chess, Go, autonomous driving, or facial recognition. AI solutions aren't currently versatile like we humans are. But they can beat even the best human player at a game of Chess or Go.
Criticism of AI
While there are many promising aspects of AI, its use also raises some concerns. For example:
- Who is responsible when things go wrong in an application powered by AI?
- How can we identify when AI systems exhibit bias due to training data or hidden algorithms?
- What will happen when an AI surpasses human intelligence?
Connections to the Australian Curriculum:
In the Digital Technologies learning area, AI:
- provides valid and interesting context and applications for curriculum skills and knowledge about data and algorithms
- is a good application of computational thinking by demonstrating abstraction and decomposition (for example, in Artificial Neural Networks)
- is an industry-relevant area of computer science
- is relevant to student personal experience, eg social media or Netflix algorithms
- is rich in potential real-world applications for investigation, eg driverless cars and face recognition
- is highly suited to a discussion of the ethical and social impacts of technology
- is scalable for understanding concepts, from primary through to secondary students.
Connections to General Capabilities and other learning areas include:
- The study of AI provides a mirror for students to reflect on their learning strategies to become more effective learners.
- Issues arising from the use of AI solutions touch ethical, social and intercultural areas.
- Machine Learning solutions frequently draw on bodies of data from fields as diverse as History, Geography, Media Studies and English.
- Artificial Neural Networks mimic the biological brain, connecting to Biology.
See the Curriculum Links section for more detail.
In summary
AI is about making computers smarter by allowing them to learn. Machine Learning is the application of AI using big data and powerful computing. Some of the approaches in Machine Learning are inspired by biology.
This information was developed in collaboration with Computer Science Education Research (CSER) group and The Digital Technologies Institute.
Image: geralt/pixabay
Artificial Intelligence Explainers: Video 1: Introduction to AI & machine learning
Use this video to introduce Artificial Intelligence (AI).
Artificial Intelligence Explainers: Video 2: AI in our everyday life
Use this video to discuss how Artificial Intelligence (AI) impacts our everyday life.
Artificial Intelligence Explainers: Video 3: AI systems
Use this video to discuss how Artificial Intelligence (AI) systems work, their benefits and potential risks.
Artificial Intelligence Explainers: Video 4: AI applications
Use this video to discuss how Artificial Intelligence (AI) has the potential to be a transformative technology much like introduction of the car, 100 years ago.
What is AI? We drew you a flowchart to work it out
This article helps define artificial intelligence, a term that is constantly evolving, and often defined in a variety of ways. The flowchart helps you work out whether something is using AI or not.
Artificial Intelligence: Australia’s Ethics Framework
This document was used for the purpose of public consultation to seek views on the discussion paper developed by Data61: Artificial Intelligence: Australia’s Ethics Framework. Feedback informed the Government’s approach to AI ethics in Australia.
What is Machine Learning? (2 mins)
A brief video that concisely defines AI, then defines machine learning through a simple example.
WEBINAR: Interactive learning and AI in Education
This ACER webinar discusses how teachers can harness the technology of AI and interactive learning techniques to improve student outcomes.
AI for Earth provides examples of projects that use artificial intelligence to address four critical areas that are vital for building a sustainable future. This page also provides a video with a useful overview.
Demystifying Artificial Intelligence (54 mins)
A webinar focused on how we can teach AI to students. It explores underpinning concepts of AI, from simple algorithms through to machine learning from data sets. Dr Joshua Ho shares his experience on developing AI classroom activities for primary school students to demystify the concept of AI.
A short, self-paced lesson with interactive activities. Introduces AI via decision trees and machine learning, with an emphasis on positive and negative social impacts.
Free, but registration required. (Additional paid lessons available, incorporating Cosmo robot.)
CS Field Guide: Artificial Intelligence
A short chapter from an online text for school students, including interactive elements. Focuses on the illusion of Intelligence. Introduces chatbots, with and without machine learning, and the Turing Test.
Free.
A series of interactive activities that visualise and explain processes of machine learning with neural Networks.
Free for teachers. Subscription cost for students. Registration required after the first activity.
A long, self-paced course taking the form of textual and graphical content followed by exercises. Covers numerous AI concepts and applications.
Free, but registration required.
AI for Earth: Helping save the planet with data science
Wee Hyong Tok is a data scientist. In this article he explains how AI can help monitor the health of the planet.
Explore how binary numbers work, how a computer adds, subtracts and how it remembers data. See algorithms at work when a computer performs multiplication and division. See mathematics in computer science when calculating Fibonacci numbers. Create beautiful pixel graphics and explore the foundations of artificial intelligence (AI).
Teachers register free, subscription cost for students.
How AI Will Improve CRM in 2019
Customer relationship management (CRM) is one of the fields that stands to benefit the most from the rapid development of artificial intelligence (AI) in 2019. Find out more.
Artificial Intelligence Roadmap
Download and view this report describing Australia’s artificial intelligence roadmap, developed by CSIRO’s Data61 for the Australian Government.
How to teach it
Unplugged lessons
These lesson plans cover a range of specialisations and subsets associated with Artificial Intelligence, colour-coded and filtered for your convenience.
AI ethics – What's possible, probable and preferred?
The development and ubiquity of artificial intelligence raise a number of social and ethical matters that students can explore in the Digital Technologies classroom. This lesson idea outlines a project to help students frame such discussions using the curriculum key idea of Creating preferred futures, tying into Critical and Creative Thinking.
Use the tasks in this lesson to introduce concepts that underpin artificial intelligence (AI). The majority of the tasks are unplugged (do not require a digital device).
We often converse with automatic chatbots for customer service without even knowing. But how do these services work? Is there artificial intelligence (AI) in them? Three projects are offered to cater for student interest and different programming skill levels.
This lesson introduces some of the skills and concepts involved with systems thinking. Students are introduced to a number of habits of a systems thinker, positive and negative feedback loops, and the concept of supra- and sub-systems.
Systems thinking and AI applications
In this lesson, students will study and analyse real-world systems involving an AI component, then envision the application of AI within a new or existing system to solve another problem of their choice.
Can a computer recognise your sentiment?
This lesson plan enables students to explore how Natural Language Processing (NLP), a subset of Artificial Intelligence (AI), is used to assess and categorise a user’s online comments. (AI is the ability of machines to mimic human capabilities in a way that we would consider 'smart'.)
Fun projects with language translation
Investigate home automation systems, including those powered by artificial intelligence (AI) with speech recognition capability. These suggested activities provide a level of differentiation to cater for students’ range of programming skills.
Artificial intelligence (AI) can sometimes be biased to certain shapes or colours. When such AI systems are applied to situations that involve people, then this bias can manifest itself as bias against skin colour or gender. This lesson explores bias in AI, where it comes from and what can be done to prevent it.
Home automation is all the rage. You talk to your mobile phone to control the lights, the fan, the air-conditioner or your pool pump. But how does it work? In this lesson, we explore the AI that could power a home automation system.
AI image recognition: exploring limitations and bias
A hands-on activity to practise training and testing an artificial intelligence (AI) model, using cartoon faces, including a discussion about sources of potential algorithmic bias and how to respond to these sources.
AI classroom activity: facial recognition
An article discussing an unplugged activity to explore machine learning for facial recognition with Primary students using data on cartoon princesses.
We can program a computer to play music. Alternatively, we can train an artificial intelligence (AI) computer about what notes go well with others, so it can play a duet with a human musician. Students can make their own instrument that plays a particular note for a set beat or they can incorporate the random function to mimic AI.
Sometimes we write and post things on social media in a hurry. Such posts can hurt people and even make them feel bullied. Wouldn't it be great if an artificial intelligence (AI) application could check our posts as we write them, and warn us if they were potentially hurtful? This lesson was developed by the Digital Technologies Institute in collaboration with DT Hub.
Discuss emotions as a class, and introduce the idea of artificial intelligence (AI). This lesson can also be used to introduce image classification – a key application of AI. Developed in collaboration with Digital Technologies Institute.
This lesson provides an opportunity to investigate security measures, including those powered by artificial intelligence (AI), that are used to protect users from unauthorised (unapproved, unwanted) access to their digital devices.
This lesson provides an opportunity for students to draw on their ethical understanding when asked to respond to different scenarios.
Analysis of AI applications, drawing on ethical understanding
This lesson plan explores the ethical aspects of artificial intelligence and the implications on our future lives.
Can AI recognise what you are drawing?
This lesson provides an opportunity to incorporate representation of data using a relevant context being studied in the classroom. Students represent an object using a line drawing, focusing on the features of the object that enable it to be easily recognised.
How can AI recognise what it sees?
This lesson is an introduction to the way in which a computer sees. It focuses on image recognition that involves feature extraction, object detection and classification.
Coding a sentimental chatbot in Python
Natural Language Processing (NLP) interprets text and speech. Chatbots provide a useful context to explore NLP. In this module students code a chatbot in Python, a conversational program capable of responding in varied ways to user input, including with the use of smart sentiment analysis.
Book analysis with AI techniques
Explore text analysis through Natural Language Processing (NLP), a significant application of artificial intelligence (AI). Watch a series of video tutorials to develop a Python program that can break down and analyse the content of a complete text, such as Robert Louis Stevenson's Treasure Island, and use smart sentiment analysis to attempt to determine the villain(s) and hero(es).
The intelligent piece of paper
An ice-breaker / thought exercise around how to define artificial intelligence (AI). A piece of paper contains instructions for always winning Noughts and Crosses, as long as it goes first. Is the piece of paper intelligent or does it just have a clever algorithm written by an intelligent person? Is this really an example of AI?
An activity involving a game played with a grid on the floor. Students model a machine learning system that learns to win the game by being punished – it loses a lolly each time it fails.
What would my preferred AI future look like?
Malyn Mawby, Head of Personalised Learning at Roseville College, explains how she implemented project-based learning (PBL) with her Year 10 class to explore artificial intelligence (AI). Through the PBL task, students selected an area of interest and investigated what is possible, probable and preferred.
Lessons using technology
Artificial intelligence…but where is the intelligence?
Use these activity ideas to explore issues related to AI including what is artificial intelligence, learning from mistakes, and how the brain actually works.
AI classroom activity: machine learning
An article discussing a coding activity (suitable to most visual or general purpose languages) that demonstrates machine learning with a searching algorithm to guess a number. The activity is then adapted to robotics by improving a robot’s bowling accuracy.
Allows you to create a machine learning data model by entering data (text, images, numbers or sounds) and grouping it with labels. You can train the AI from your data, then make programs in Scratch, Python or AppInventor using the trained AI. Over 20 pre-made worksheets for students and teachers.
This is a game built with machine learning. You draw, and a neural network tries to guess what you’re drawing. Of course, it doesn’t always work. But the more you (and others) play with it, the more it will learn. It’s just one example of how you can use machine learning in fun ways.
Using a similar system to the Google game Quick Draw!, this tool suggests clip art or icons as you sketch.
Explore some relevant APIs and applications to see how Microsoft’s AI for Earth project is advancing conservation around the world.
What would my preferred AI future look like?
Malyn Mawby, Head of Personalised Learning at Roseville College, explains how she implemented project-based learning (PBL) with her Year 10 class to explore AI. Through the PBL task, students selected an area of interest and investigated what is possible, probable and preferred.
Digital Pets at Ashwood School, Victoria
Kenneth Clark Burt explains how this P–12 specialist school uses digital pets (robots) to differentiate the Digital Technologies curriculum. The robot used incorporates AI technology.
Accelerating crop farmers’ adaptation to climate change
A case study briefly describing an AI project where scientists have used image-analysis technology in the agricultural sector.
Emulating nature’s perfect pursuit
A case study briefly describing an AI project where scientists have adapted dragonflies’ neuronal processes into a unique algorithm that emulates the insect’s phenomenal visual tracking capability.
Career profile: Sean Gribben, Computer Science undergraduate and part-time CSIRO research technician
This is one of a series of career profiles. It aims to inspire students to consider a STEM career by highlighting the individual's background and interests, a snap shot of their current role and the pathways they have taken to enter their profession. Sean Gribben is a Computer Science undergraduate and part-time CSIRO research technician.
| Relevant content descriptor | Notes | |
| ACTDIP016 | Acquire, store and validate different types of data, and use a range of software to interpret and visualise data to create information |
Training a machine learning system requires access to data. For Supervised Learning, the data must typically be labelled and categorised. AI is now part of the range of solutions used to interpret data and make predictions. EXAMPLE: Images of faces are used to train AI to successfully recognise human emotions. |
| ACTDIP017 | Define problems in terms of data and functional requirements drawing on previously solved problems |
This is the first step in a problem-solving methodology for creating a digital solution. (Content descriptors 018, 019, 020, 021 make up the remaining steps.) AI has been developed to deal with diverse (‘fuzzy’) data, i.e. data that varies greatly and deviates from an ideal case. Students can determine when to consider an AI solution or when a classical programmed solution is suited. EXAMPLE: Recognising handwriting, images or traffic, are scenarios with ‘fuzzy’ input data possibly suited to an AI solution. |
| ACTDIP018 | Design a user interface for a digital system |
AI is now part of many user interfaces. Machine Learning systems can curate, advise and orchestrate system behaviour by observing the user’s behaviour and that of other users. AI is also used for natural language processing, allowing for better speech recognition. EXAMPLE: Your smartphone remembers the places you visit and the people you call and can therefore provide personalised recommendations. |
| ACTDIP019 | Design, modify and follow simple algorithms involving sequences of steps, branching, and iteration (repetition) |
Along with data, algorithms are a key part of AI. A typical AI learning algorithm consists of three phases:
The Learning process incorporates decisions (branching) and loops (iteration). Students can learn how an AI system actually organises data such that it can draw conclusions. EXAMPLE: Simulate an Artificial Neural Network at mycomputerbrain. Algorithms are also used to apply the output from a Machine Learning model (eg. if I recognise a verbal command to “make it darker in here”, what action might be taken?) EXAMPLE: Netflix, Facebook and YouTube use algorithms to recommend or show media to each user. These recommendations may be based on a Machine Learning model trained from other users’ behaviour. |
| ACTDIP020 | Implement digital solutions as simple visual programs involving branching, iteration (repetition), and user input |
The actual coding of algorithms relating to AI is possible in both visual and general-purpose languages. EXAMPLE: Software such as Machine Learning For Kids allows students to use either Scratch or Python to code algorithms that take advantage of data models they’ve trained up. |
| ACTDIP021 | Explain how student solutions and existing information systems are sustainable and meet current and future local community needs |
There is room here for discussion of existing AI systems used for social and environmental purposes, with positive and negative impacts. EXAMPLE: AI is used for facial recognition systems, with applications in surveillance and social media. It is also used to recognise endangered animals from thousands of photographs, saving environmental scientists precious time. Machine learning results come with a confidence value, eg. a 26% confidence that an image shows a cat. Students need to understand the implications. EXAMPLE: Should a car brake if it detects a pedestrian on the road with a 40% confidence? |
| Relevant content descriptor | Notes | |
| ACTDIP025 | Acquire data from a range of sources and evaluate authenticity, accuracy and timeliness |
Training a machine learning system requires access to data. The effectiveness of an AI system depends on the quality and variety of the data on which is was trained. Also, biased data is a common factor towards Algorithmic Bias in the AI’s behaviour. EXAMPLE: Algorithmic bias has appeared in AI systems which were less accurate at recognising gender in faces from certain ethnic groups. |
| ACTDIP026 | Analyse and visualise data using a range of software to create information, and use structured data to model objects or events |
For supervised learning, the data must typically be labelled and categorised. EXAMPLE: Students could label images that contain either cats or dogs for training a Machine Learning system. The labels must be attached in a format that the system’s algorithm can understand. AI is also part of the range of solutions now used to interpret data. EXAMPLE: AI systems can analyse newspaper articles and other text sources to identify tone and style. |
| ACTDIP027 | Define and decompose real-world problems taking into account functional requirements and economic, environmental, social, technical and usability constraints |
This is the first step in a problem-solving methodology for creating a digital solution. (Content descriptors 028, 029, 030, 031 make up the remaining steps.) AI has been developed to deal with diverse (‘fuzzy’) data, i.e. data that varies greatly and deviates from an ideal case. Students can determine when to consider an AI solution or when a classical programmed solution is suited. EXAMPLE: Recognising handwriting, images or traffic, are scenarios with ‘fuzzy’ input data possibly suited to an AI solution. |
| ACTDIP028 | Design the user experience of a digital system, generating, evaluating and communicating alternative designs |
AI is now part of many user interfaces. Machine Learning systems can curate, advise and orchestrate system behaviour by observing the user’s behaviour and that of other users. AI is also used for natural language processing, allowing for a more personalised user experience. EXAMPLE: Your smartphone remembers the places you visit and the people you call and can therefore provide personalised recommendations. |
| ACTDIP029 | Design algorithms represented diagrammatically and in English, and trace algorithms to predict output for a given input and to identify errors |
Along with data, algorithms are a key part of AI. A typical AI learning algorithm consists of three phases:
The Learning process incorporates decisions (branching) and loops (iteration). Students can learn how an AI system actually organises data such that it can draw conclusions. EXAMPLE: Simulate an Artificial Neural Network at mycomputerbrain. Algorithms are also used to apply the output from a Machine Learning model (eg if I recognise a verbal command to 'make it darker in here', what action might be taken?) EXAMPLE: Netflix, Facebook and YouTube use algorithms to recommend or show media to each user. These recommendations may be based on a Machine Learning model trained from other users’ behaviour. |
| ACTDIP030 | Implement and modify programs with user interfaces involving branching, iteration and functions in a general-purpose programming language |
The actual coding of algorithms relating to AI is possible in both visual and general-purpose languages. EXAMPLE: Software such as Machine Learning For Kids allows students to use either Scratch or Python to code algorithms that take advantage of data models they’ve trained up. |
| ACTDIP031 | Evaluate how student solutions and existing information systems meet needs, are innovative, and take account of future risks and sustainability |
There is room here for discussion of existing AI systems used for social and environmental purposes, with positive and negative impacts. EXAMPLE: AI is used for facial recognition systems, with applications in surveillance and social media. It is also used to recognise endangered animals from thousands of photographs, saving environmental scientists precious time. Machine learning results come with a confidence value, eg. a 26% confidence that an image shows a cat. Students need to understand the implications. EXAMPLE: Should a car brake if it detects a pedestrian on the road with 40% confidence? |
| Relevant content descriptor | Notes | |
| ACTDIP036 | Develop techniques for acquiring, storing and validating quantitative and qualitative data from a range of sources, considering privacy and security requirements |
Training a machine learning system requires access to data. The effectiveness of an AI system depends on the quality and variety of the data on which is was trained. Also, biased data is a common factor towards Algorithmic Bias in the AI’s behaviour. EXAMPLE: Algorithmic bias has appeared in AI systems which were less accurate at recognising gender in faces from certain ethnic groups. |
| ACTDIP037 | Analyse and visualise data to create information and address complex problems, and model processes, entities and their relationships using structured data |
For supervised learning, the data must typically be labelled and categorised. EXAMPLE: Students could label images that contain either cats or dogs for training a Machine Learning system. The labels must be attached in a format that the system’s algorithm can understand. AI is also part of the range of solutions now used to interpret data. EXAMPLE: AI systems can analyse newspaper articles and other text sources to identify tone and style. |
| ACTDIP038 | Define and decompose real-world problems precisely, taking into account functional and non-functional requirements and including interviewing stakeholders to identify needs |
This is the first step in a problem-solving methodology for creating a digital solution. (Content descriptors 039, 040, 041, 042 make up the remaining steps.) AI has been developed to deal with diverse (‘fuzzy’) data, i.e. data that varies greatly and deviates from an ideal case. Students can determine when to consider an AI solution or when a classical programmed solution is suited. EXAMPLE: Recognising handwriting, images or traffic, are scenarios with ‘fuzzy’ input data possibly suited to an AI solution. |
| ACTDIP039 | Design the user experience of a digital system by evaluating alternative designs against criteria including functionality, accessibility, usability, and aesthetics |
AI is now part of many user interfaces. Machine Learning systems can curate, advise and orchestrate system behaviour by observing the user’s behaviour and that of other users. AI is also used for natural language processing, allowing for a more personalised user experience EXAMPLE: Your smartphone remembers the places you visit and the people you call and can therefore provide personalised recommendations. |
| ACTDIP040 | Design algorithms represented diagrammatically and in structured English and validate algorithms and programs through tracing and test cases |
Along with data, algorithms are a key part of AI. A typical AI learning algorithm consists of three phases:
The Learning process incorporates decisions (branching) and loops (iteration). Students can learn how an AI system actually organises data such that it can draw conclusions. EXAMPLE: Simulate an Artificial Neural Network at mycomputerbrain. Algorithms are also used to apply the output from a Machine Learning model (eg if I recognise a verbal command to 'make it darker in here', what action might be taken?) EXAMPLE: Netflix, Facebook and YouTube use algorithms to recommend or show media to each user. These recommendations may be based on a Machine Learning model trained from other users’ behaviour. |
| ACTDIP041 | Implement modular programs, applying selected algorithms and data structures including using an object-oriented programming language |
The actual coding of algorithms relating to AI is possible in both visual and general-purpose languages. EXAMPLE: Software such as Machine Learning For Kids allows students to use either Scratch or Python to code algorithms that take advantage of data models they’ve trained up. |
| ACTDIP042 | Evaluate critically how student solutions and existing information systems and policies, take account of future risks and sustainability and provide opportunities for innovation and enterprise |
There is room here for discussion of existing AI systems used for social and environmental purposes, with positive and negative impacts. EXAMPLE: AI is used for facial recognition systems, with applications in surveillance and social media. It is also used to recognise endangered animals from thousands of photographs, saving environmental scientists precious time. Machine learning results come with a confidence value, eg. a 26% confidence that an image shows a cat. Students need to understand the implications. EXAMPLE: Should a car brake if it detects a pedestrian on the road with 40% confidence? |
The topic of AI connects well to the following General Capabilities:
Literacy:
AI systems can be used to recognise and analyse texts and other media, as well as to compose texts and other media. AI may serve as a useful ‘counter-perspective’ when interpreting and evaluating texts. How might an AI reason differently to a human?
A specific relevant field of AI is natural language processing.
Numeracy:
Algorithms almost invariably involve Mathematics of some kind, because of the prevalence of large bodies of data that can be interpreted and represented statistically. For example, a Machine Learning system may be 90% certain that it has correctly recognised an image.
Most relevant elements:
- Recognising and using patterns and relationships
- Interpreting statistical information
ICT Capability:
With AI incorporated into systems used every day, it is already an element of ICT students use.
Most relevant elements:
- Applying social and ethical protocols and practices when using ICT – Identify the impacts of ICT on society
- Investigating with ICT (due to the relevance of finding and selecting data)
- Creating with ICT (already connected to Digital Technologies learning area)
- Communicating with ICT – Understand computer mediated communications
Critical and Creative Thinking:
As with the Ethical Understanding Capability, this Capability is well served by the context examples provided by investigation into AI. It also benefits from the thinking skills (computational thinking, systems thinking, design thinking) provided by adequate implementation of Digital Technologies as a Learning Area.
Personal and Social Capability:
AI issues such as those described above touch the element of Social Awareness:
- Appreciate diverse perspectives
- Contribute to civil society
- Understand relationships
Ethical Understanding:
AI provides context examples for all elements in the Ethical Understanding Capability (Understanding, Reasoning, Values), with present and future potential issues:
- personal context, eg. social media or Netflix algorithms
- global / community context, eg. automation, Algorithmic Bias, facial recognition and security
Intercultural Understanding:
Cultural biases impact AI issues, eg. religious documents used to train AI systems, or racial characteristics used to train facial recognition systems.