Computing · JC 1 · Impacts of Computing and Emerging Tech · Semester 2

Introduction to Artificial Intelligence

Understanding what AI is, its history, and common applications in daily life.

• MOE Syllabus Outcomes
• MOE: Impacts of Computing and Emerging Tech - JC1

About This Topic

Ethics in Artificial Intelligence is a critical topic as AI becomes integrated into every aspect of our lives. Students explore the moral responsibilities of developers, focusing on issues like algorithmic bias, where AI models can inherit and amplify human prejudices. We also discuss the impact of automation on the workforce and the need for transparency in AI decision-making.

In the Singapore context, where we are a leader in AI adoption through initiatives like AI Singapore, these ethical considerations are not just theoretical. Students look at real-world examples, such as AI in hiring or facial recognition in public spaces, and consider how to balance innovation with fairness and accountability. This topic particularly benefits from hands-on, student-centered approaches like structured debates and case study analysis.

Key Questions

1. Explain the fundamental concepts behind Artificial Intelligence.
2. Analyze examples of AI in everyday technology.
3. Predict future applications of AI based on current trends.

Learning Objectives

• Explain the core principles of machine learning and deep learning.
• Analyze common AI applications in sectors like healthcare, finance, and transportation.
• Classify different types of AI, such as supervised, unsupervised, and reinforcement learning.
• Evaluate the historical progression of AI development from early concepts to modern systems.

Before You Start

Introduction to Computer Systems

Why: Students need a foundational understanding of how computers process information and execute instructions to grasp AI concepts.

Data Representation and Structures

Why: AI heavily relies on data, so familiarity with how data is organized and stored is essential for understanding ML and DL.

Key Vocabulary

Artificial Intelligence (AI)	The simulation of human intelligence processes by machines, especially computer systems. This includes learning, problem-solving, and decision-making.
Machine Learning (ML)	A subset of AI that enables systems to learn from data and improve performance on a task without being explicitly programmed. It involves algorithms that identify patterns.
Deep Learning (DL)	A subset of ML that uses artificial neural networks with multiple layers (deep architectures) to learn complex patterns from large amounts of data.
Neural Network	A computing system inspired by the biological neural networks that constitute animal brains. It consists of interconnected nodes or neurons that process information.
Algorithm	A set of rules or instructions followed in calculations or other problem-solving operations, especially by a computer. In AI, algorithms enable learning and decision-making.

Watch Out for These Misconceptions

Common Misconception

AI is objective because it is based on math and data.

What to Teach Instead

AI is only as objective as the data it is trained on. If the data contains human bias, the AI will learn and repeat that bias. A 'biased data' simulation where students 'train' a simple classifier helps them see this firsthand.

Common Misconception

AI will eventually replace all human jobs.

What to Teach Instead

While AI will automate some tasks, it also creates new roles and changes the nature of existing ones. Peer discussion about 'human-only skills' (like empathy and complex ethics) helps students see AI as a tool rather than a total replacement.

Active Learning Ideas

Formal Debate: The Trolley Problem for AI

Students are assigned to represent different stakeholders (car manufacturers, passengers, pedestrians) in a debate about how a self-driving car should be programmed to act in an unavoidable accident scenario.

45 min·Whole Class

Inquiry Circle: Bias in the Machine

Groups are given a sample dataset used to train a fictional 'loan approval' AI. They must identify potential sources of bias (e.g., historical data that favors certain demographics) and propose ways to make the model fairer.

40 min·Small Groups

Gallery Walk: AI in My Life

Students create posters showing an AI application they use (e.g., TikTok's algorithm, ChatGPT). They must identify one ethical risk and one benefit for each application, then move around the room to comment on their peers' findings.

30 min·Small Groups

Real-World Connections

• Singapore's Changi Airport utilizes AI-powered facial recognition for automated immigration clearance, speeding up passenger processing and enhancing security.
• Online retail platforms like Shopee and Lazada in Singapore employ AI algorithms to personalize product recommendations, predict customer purchasing behavior, and optimize inventory management.
• The Monetary Authority of Singapore (MAS) is exploring AI for fraud detection and risk assessment in financial services, aiming to create a more secure and efficient banking system.

Assessment Ideas

Quick Check

Present students with three scenarios: (1) a spam filter identifying unwanted emails, (2) a navigation app suggesting the fastest route, and (3) a chatbot answering customer queries. Ask students to identify which scenario best represents machine learning and explain why.

Discussion Prompt

Facilitate a class discussion using the prompt: 'Beyond recommendation engines and virtual assistants, what is one less obvious application of AI you have encountered or can imagine? How does it function at a basic level?' Encourage students to share specific examples and explain the AI's role.

Exit Ticket

On an index card, have students write down one historical milestone in AI development and one potential future application of AI. Ask them to briefly explain the significance of the milestone and the impact of the future application.

Frequently Asked Questions

What is algorithmic bias?

Algorithmic bias occurs when an AI system produces results that are systematically prejudiced against certain groups of people. This usually happens because the data used to train the AI reflects existing societal biases or because the developers made flawed assumptions during the design process.

Who is responsible when an AI makes a mistake?

This is a complex legal and ethical question. Depending on the situation, responsibility could lie with the developers, the company that deployed the AI, or even the users. In Singapore, the Model AI Governance Framework provides guidelines to help organizations manage these risks responsibly.

How can active learning help students understand AI ethics?

Active learning through 'Role-Play' and 'Case Studies' is the best way to teach ethics. By stepping into the shoes of a person affected by a biased algorithm, students develop empathy and a deeper understanding of the real-world consequences of technical decisions. It moves the conversation from 'what is possible' to 'what is right,' which is the core of ethical thinking.

What does 'explainable AI' mean?

Explainable AI (XAI) refers to AI systems that are designed so that their decisions and actions can be understood and explained by humans. This is important for building trust, especially in high-stakes areas like healthcare, finance, and criminal justice.