Computing · JC 2

Active learning ideas

Ethics and Professional Conduct in IT

Ethics in AI benefits from active learning because students grapple with real-world consequences of abstract concepts. When they examine biased datasets or debate facial recognition policies, they see how technical choices translate into human impacts. This makes the abstract tangible and the moral stakes clear.

• MOE Syllabus Outcomes
• MOE H2 Computing (Syllabus 9569), Integrated Topics - Social, Ethical and Legal Issues in Computing
• MOE H2 Computing (Syllabus 9569), Integrated Topics - Intellectual Property and Copyright

• 30–50 min
• Pairs → Whole Class
• 4 activities

Activity 01

• Four Corners
• 45 min · Pairs

Debate Pairs: Facial Recognition Limits

Assign pairs to affirm or oppose public use of facial recognition. Provide case studies on privacy vs. security. Pairs prepare 3-minute arguments, then switch sides for rebuttals. Conclude with whole-class vote and reflection.

How do we determine if a technological innovation is ethical?

Facilitation Tip

During Debate Pairs: Facial Recognition Limits, assign one student to argue for strict regulation and one to argue for minimal restriction to force nuanced positions.

What to look for

Present students with a scenario: An AI system used for loan applications denies a loan to a qualified applicant from a historically marginalized community. Ask: 'Who is primarily responsible for this discriminatory outcome: the data scientists, the company deploying the AI, the users of the AI, or the creators of the original biased data? Justify your answer with reference to at least two ethical principles.'

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

Activity 02

• Four Corners
• 50 min · Small Groups

Small Groups: Bias Audit Simulation

Give groups sample datasets with hidden biases, like loan approval records skewed by gender. Groups identify biases, propose fixes, and test revised data on mock algorithms using spreadsheets. Share findings in a gallery walk.

What are the implications of open-source versus proprietary software?

Facilitation Tip

During Small Groups: Bias Audit Simulation, provide a small, labeled dataset so students can manually spot underrepresentation or skewed labels.

What to look for

Provide students with a short description of an AI system (e.g., a content moderation AI, a medical diagnostic AI). Ask them to identify one potential source of bias in its training data and one potential negative societal consequence if that bias is not addressed. Have them write their answers on a shared digital whiteboard.

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

Activity 03

• Four Corners
• 40 min · Whole Class

Role-Play: Whole Class Trolley Problem

Present AI car crash scenarios where the vehicle must choose between harms. Students draw roles: AI designer, victim families, regulator. Role-play discussions, then vote on programming choices and justify positions.

How should IT professionals handle conflicts of interest?

Facilitation Tip

During Role-Play: Whole Class Trolley Problem, assign roles with conflicting values (e.g., safety engineer, community advocate) to surface ethical trade-offs.

What to look for

Students work in pairs to identify a news article about an AI ethical issue. They then present the article's core problem to another pair. The assessing pair must identify the type of bias involved (e.g., selection bias, measurement bias) and suggest one concrete step the AI developers could take to mitigate it. Assessors provide feedback on the clarity and feasibility of the suggested mitigation.

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

Activity 04

• Four Corners
• 30 min · Individual

Individual: Ethical Dilemma Journal

Students read a case on AI in hiring, note biases and stakeholders. Write personal stances with pros/cons. Pair-share journals, then discuss class patterns in ethical trade-offs.

How do we determine if a technological innovation is ethical?

Facilitation Tip

During Individual: Ethical Dilemma Journal, ask students to revisit entries weekly to track how their reasoning evolves.

What to look for

Present students with a scenario: An AI system used for loan applications denies a loan to a qualified applicant from a historically marginalized community. Ask: 'Who is primarily responsible for this discriminatory outcome: the data scientists, the company deploying the AI, the users of the AI, or the creators of the original biased data? Justify your answer with reference to at least two ethical principles.'

• Understand
• Analyze
• Evaluate
• Self-Awareness
• Social Awareness

A few notes on teaching this unit

Teachers should avoid presenting ethics as a purely philosophical exercise disconnected from technical work. Instead, integrate ethical analysis into data science skills, like asking students to audit datasets for bias before training models. Research suggests students retain ethical reasoning better when they apply it to concrete cases rather than abstract principles. Use structured debates and simulations to make invisible biases visible.

Successful learning looks like students articulating specific sources of bias, defending ethical positions with evidence, and proposing actionable solutions. They should move beyond 'AI is bad' to 'Here is how bias occurs and here is how to address it.'

---

Watch Out for These Misconceptions

• During Debate Pairs: Facial Recognition Limits, some students may claim AI is unbiased if trained on 'enough' data.

  During Debate Pairs, have students examine a sample dataset with known underrepresentation. Ask them to identify which groups are missing and how that might affect the AI's accuracy.

• During Role-Play: Whole Class Trolley Problem, students often assume AI decisions are solely the developer's responsibility.

  During Role-Play, assign roles that include users, regulators, and affected communities. Require each role to justify their share of accountability in the final decision.

• During Small Groups: Bias Audit Simulation, students may think ethics is a post-deployment fix rather than a design constraint.

  During Small Groups, provide case studies where bias mitigation techniques were integrated into model development. Ask students to compare outcomes with and without these constraints.

---

Methods used in this brief

• Four Corners