Ethical Dilemmas of AI

Active learning works for ethical dilemmas of AI because abstract concepts like bias and accountability become concrete when students see real-world consequences. Role-plays and debates let students test their own assumptions, turning quiet reflection into shared reasoning. This topic demands more than reading; it needs immediate, collaborative analysis to shift perspectives from passive acceptance to critical assessment.

• Year 9
• Computing
• 4 activities
• 30 min–50 min

Learning Objectives

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

Formal Debate

⏱ 45 min·Pairs

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.

Prepare & details

Who should be held responsible when an AI-driven system causes harm?

Facilitation Tip: During Debate Pairs, set a strict three-minute speaking limit per turn to keep discussions focused and give both voices equal weight.

Setup: Two teams facing each other, audience seating for the rest

Materials: Debate proposition card, Research brief for each side, Judging rubric for audience, Timer

• Analyze
• Evaluate
• Create
• Self-Management
• Decision-Making

Formal Debate

⏱ 50 min·Small Groups

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.

Prepare & details

Analyze how algorithmic bias can perpetuate and amplify societal inequalities.

Facilitation Tip: For the Case Study Carousel, provide sticky notes for students to mark patterns they see across examples, then cluster these notes to reveal systemic bias.

Setup: Two teams facing each other, audience seating for the rest

Materials: Debate proposition card, Research brief for each side, Judging rubric for audience, Timer

• Analyze
• Evaluate
• Create
• Self-Management
• Decision-Making

Formal Debate

⏱ 40 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.

Prepare & details

Predict the long-term impact of widespread AI automation on the global workforce.

Facilitation Tip: In Role-Play Scenarios, assign students roles they wouldn’t normally choose to stretch their empathy and expose blind spots in workforce impacts.

Setup: Two teams facing each other, audience seating for the rest

Materials: Debate proposition card, Research brief for each side, Judging rubric for audience, Timer

• Analyze
• Evaluate
• Create
• Self-Management
• Decision-Making

Formal Debate

⏱ 30 min·Whole Class

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.

Prepare & details

Who should be held responsible when an AI-driven system causes harm?

Setup: Two teams facing each other, audience seating for the rest

Materials: Debate proposition card, Research brief for each side, Judging rubric for audience, Timer

• Analyze
• Evaluate
• Create
• Self-Management
• Decision-Making

Teaching This Topic

Teachers should frame AI ethics as a chain of human decisions, not a technical failure, to avoid framing bias as an algorithmic glitch. Use structured debates to slow down fast opinions, giving students time to gather evidence before reacting. Research shows that when students role-play stakeholders, they’re more likely to consider long-term societal impacts rather than short-term convenience.

What to Expect

Successful learning looks like students using evidence to justify their positions, not just stating opinions. They should trace ethical chains from data to decision-making and identify where responsibility lies. By the end, students will articulate nuanced trade-offs, not simple right or wrong answers.

Watch Out for These Misconceptions

Common Misconception

During Debate Pairs on AI Accountability, watch for students assuming algorithms are neutral because their creators intend them to be fair.

What to Teach Instead

Use the debate’s structure to push students to examine training data sources and developer assumptions from the Case Study Carousel materials, forcing them to connect abstract intent to concrete bias patterns.

Common Misconception

During Role-Play Scenarios on Job Displacement, watch for students predicting total job loss based on fear rather than sector-specific evidence.

What to Teach Instead

Have students refer to real labor market data provided in the scenario cards to ground their predictions, then revise their role-play scripts to match evidence rather than alarmist claims.

Common Misconception

During Ethical Dilemma Cards, watch for students assigning blame only to programmers without considering broader chains of responsibility.

What to Teach Instead

Use the mapping activity from Ethical Dilemma Cards to trace each card’s scenario from data collection to deployment, highlighting where non-technical stakeholders share accountability.

Assessment Ideas

Discussion Prompt

After Debate Pairs on AI Accountability, present the mental health chatbot scenario. Ask students to reference their debate reasoning to assign shared responsibility across developers, deployers, and users, then justify their ranking with specific evidence.

Exit Ticket

After Ethical Dilemma Cards, ask students to write one AI technology they use and one ethical issue tied to it, then categorize the severity of harm and explain their reasoning in two sentences.

Quick Check

During Case Study Carousel, display images of AI applications and have students categorize each as high or low risk for bias. On the back of their sticky notes, require one sentence explaining their category choice using language from the case studies.

Extensions & Scaffolding

• Challenge early finishers to design a policy brief proposing one concrete regulation for AI hiring tools, citing evidence from their Case Study Carousel findings.
• Scaffolding for struggling students: provide sentence starters like 'This bias matters because...' and 'The responsibility chain looks like...' to guide their analysis in Ethical Dilemma Cards.
• Deeper exploration: invite a local tech worker or ethicist to join the Job Displacement role-play and respond to student proposals in real time.

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.