Computing · Year 9 · Data Science and Society · Summer Term

Ethical Dilemmas of AI

Students will discuss the ethical implications of AI, such as bias, accountability, and job displacement.

• National Curriculum Attainment Targets
• KS3: Computing - Impact of Technology
• KS3: Computing - Ethics and Law

About This Topic

Ethical dilemmas of AI challenge students to examine bias in algorithms, accountability for system failures, and job displacement from automation. In Year 9, they tackle key questions: who bears responsibility when AI causes harm, how bias reinforces inequalities, and what widespread automation means for the workforce. This topic fits KS3 Computing standards on technology's impact and ethics, encouraging analysis of real-world cases like facial recognition errors or hiring algorithms that favour certain groups.

Within the Data Science and Society unit, discussions build skills in critical evaluation, evidence-based arguments, and empathy for diverse perspectives. Students connect technical knowledge from prior units to societal consequences, fostering responsible digital citizenship. Structured debates reveal how personal values shape ethical views, preparing them for complex decisions in an AI-driven world.

Active learning suits this topic perfectly. Role-plays of AI mishaps make abstract issues immediate and personal, while group deliberations expose varied viewpoints. These methods deepen understanding through peer challenge and reflection, turning passive listeners into engaged ethical thinkers.

Key Questions

1. Who should be held responsible when an AI-driven system causes harm?
2. Analyze how algorithmic bias can perpetuate and amplify societal inequalities.
3. Predict the long-term impact of widespread AI automation on the global workforce.

Learning Objectives

• Critique real-world AI applications for potential ethical risks, such as algorithmic bias or lack of transparency.
• Analyze the societal impact of AI-driven job displacement and propose mitigation strategies.
• Evaluate arguments regarding accountability for AI system failures, considering developers, users, and the AI itself.
• Synthesize information from case studies to construct a reasoned argument about the fairness of a specific AI deployment.

Before You Start

Introduction to Artificial Intelligence

Why: Students need a basic understanding of what AI is and how it functions to discuss its ethical implications.

Data Representation and Interpretation

Why: Understanding how data is collected and interpreted is crucial for grasping the concept of algorithmic bias.

Key Vocabulary

Algorithmic Bias	Systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.
Accountability	The obligation of an individual or organization to accept responsibility for their actions and decisions, especially when AI systems cause harm.
Job Displacement	The loss of employment due to technological change, specifically in this context, the automation of tasks previously performed by humans.
AI Ethics	A field of study concerned with the moral implications of artificial intelligence, including its design, development, and deployment.
Transparency	The principle that the workings of an AI system, including its decision-making processes, should be understandable and explainable.

Watch Out for These Misconceptions

Common Misconception

AI systems are always neutral and unbiased.

What to Teach Instead

Algorithms reflect biases in training data from human sources. Group analysis of real cases, like skewed facial recognition, helps students spot patterns and propose fixes. Active discussions reveal how unchecked bias harms marginalised groups.

Common Misconception

AI will eliminate all human jobs soon.

What to Teach Instead

Automation displaces some roles but creates others, with impacts varying by sector. Role-plays of future scenarios let students explore evidence on job evolution. Peer debates build nuanced predictions over alarmist views.

Common Misconception

Only programmers are accountable for AI harm.

What to Teach Instead

Responsibility chains from developers to deployers and regulators. Ethical mapping activities trace accountability in chains, clarifying shared duties. Collaborative mapping exposes gaps where active input clarifies complex liabilities.

Active Learning Ideas

Debate Pairs: AI Accountability

Pair students to prepare arguments for one side: 'AI developers are always responsible' versus 'End-users share blame'. Each pair presents for 3 minutes, then switches sides. Class votes and discusses shifts in perspective.

45 min·Pairs

Case Study Carousel: Bias Examples

Divide class into small groups with cases like biased loan algorithms or recruitment tools. Groups analyse causes, impacts, and solutions on posters, then rotate to add feedback. Conclude with whole-class synthesis.

50 min·Small Groups

Role-Play Scenarios: Job Displacement

Assign roles like factory worker, CEO, policymaker in an automation scenario. Groups act out a town hall meeting, negotiating solutions. Debrief on trade-offs and ethical priorities.

40 min·Small Groups

Ethical Dilemma Cards: Whole Class Vote

Distribute cards with dilemmas like self-driving car choices. Students vote anonymously via polls, then discuss in whole class why choices vary and what principles guide them.

30 min·Whole Class

Real-World Connections

• Facial recognition software used by law enforcement agencies has faced criticism for higher error rates with certain demographic groups, raising questions about bias and fairness.
• Automated hiring tools, like those used by some large tech companies, can inadvertently filter out qualified candidates based on patterns learned from historical data, potentially perpetuating workplace inequalities.
• Autonomous vehicle developers, such as Waymo and Cruise, grapple with the ethical dilemma of programming vehicles to make split-second decisions in unavoidable accident scenarios.

Assessment Ideas

Discussion Prompt

Present students with a scenario: An AI chatbot used by a mental health service provides harmful advice. Ask: 'Who is most responsible for the harm caused: the AI developers, the company deploying the chatbot, or the user who followed the advice? Justify your answer with specific reasoning.'

Exit Ticket

Ask students to write down one AI technology they use or are aware of. Then, have them identify one potential ethical issue associated with it and briefly explain why it is a concern.

Quick Check

Display images or short descriptions of different AI applications (e.g., recommendation algorithms, medical diagnostic tools, AI art generators). Ask students to quickly categorize each as having a high or low risk of algorithmic bias and provide a one-sentence justification.

Frequently Asked Questions

How can I teach AI bias effectively in Year 9?

Use real datasets showing gender or racial skews in tools like image classifiers. Have students audit sample data in pairs, then redesign fairer versions. This hands-on approach reveals bias sources and builds data literacy, aligning with KS3 ethics standards.

What active learning strategies work for AI ethics?

Debates, role-plays, and dilemma carousels engage students actively. These methods prompt them to defend positions, empathise with stakeholders, and refine arguments through peer feedback. Such experiences make ethics memorable and applicable, far beyond lectures.

Real-world examples for AI job displacement?

Discuss Amazon's warehouse robots reducing manual roles or AI tutors aiding teachers. Students chart job shifts in pairs, using reports from sources like the UK Office for National Statistics. This grounds predictions in data, sparking informed workforce debates.

How to assess understanding of AI accountability?

Use structured reflections after debates: students write who is responsible in a scenario and why, citing evidence. Rubrics score reasoning depth and alternatives considered. Portfolios of case analyses track growth in ethical nuance over the unit.