Computing · Year 6 · The Impact of Technology on Society · Summer Term

Ethical Considerations of AI

Students discuss the ethical implications of AI making decisions, especially in sensitive areas like health or safety.

• National Curriculum Attainment Targets
• KS2: Computing - Digital Literacy
• KS2: Computing - Online Safety

About This Topic

Ethical considerations of AI guide Year 6 students to examine the moral challenges of intelligent systems making decisions in areas like health or safety. They critique applications such as AI diagnosing medical conditions or managing traffic in self-driving cars, predict dilemmas like algorithmic bias or accountability gaps, and design guidelines for AI use in schools. These activities build on prior computing knowledge of algorithms and data, linking directly to KS2 digital literacy and online safety standards.

This topic fosters critical thinking, empathy, and civic responsibility within the UK National Curriculum. Students confront real-world issues, such as how biased training data can lead to unfair outcomes, and learn to balance technological benefits with human values. Discussions reveal diverse perspectives, strengthening communication skills and preparing pupils for informed participation in a digital society.

Active learning benefits this topic because ethics involve nuanced, subjective viewpoints best explored through interaction. Role-plays, debates, and collaborative design make abstract concerns personal and immediate. Students negotiate ideas, defend positions, and refine guidelines together, which deepens understanding and equips them to navigate future AI developments thoughtfully.

Key Questions

1. Critique the idea of AI making decisions about human health or safety.
2. Predict potential ethical dilemmas that could arise from advanced AI.
3. Design a set of ethical guidelines for the use of AI in schools.

Learning Objectives

• Critique the potential for algorithmic bias in AI decision-making systems used in healthcare or public safety.
• Analyze the ethical implications of AI accountability when errors occur in automated systems.
• Design a set of ethical guidelines for the responsible use of AI in a school environment.
• Compare different perspectives on AI's role in making life-altering decisions.

Before You Start

Introduction to Algorithms

Why: Students need a basic understanding of how algorithms work to grasp how AI systems make decisions.

Data and Information

Why: Understanding that AI systems are trained on data is crucial for comprehending issues like algorithmic bias.

Key Vocabulary

Algorithmic Bias	Unfair outcomes produced by an AI system, often due to skewed or incomplete data used during its training.
Accountability Gap	The difficulty in assigning responsibility when an AI system makes a mistake or causes harm.
Ethical Guidelines	A set of principles or rules designed to ensure that AI is developed and used in a morally sound and fair way.
Transparency	The principle that the decision-making process of an AI system should be understandable and explainable.

Watch Out for These Misconceptions

Common Misconception

AI decisions are always neutral and fair.

What to Teach Instead

AI reflects biases in its training data, leading to unfair outcomes in health or safety. Examining sample datasets in groups helps students spot patterns, while debates encourage them to question assumptions and propose diverse data solutions.

Common Misconception

AI can fully replace human judgment in ethics.

What to Teach Instead

Humans provide context, empathy, and accountability that AI lacks. Role-plays reveal limitations through peer challenges, helping students value hybrid approaches where people oversee AI.

Common Misconception

Privacy concerns disappear with advanced AI.

What to Teach Instead

AI often requires vast personal data, risking misuse. Collaborative guideline design activities prompt students to prioritize consent and security, connecting personal stories to broader protections.

Active Learning Ideas

Debate Carousel: AI in Health Decisions

Prepare four scenario cards on AI health uses, like automated diagnosis. Divide class into small groups to prepare pro and con arguments for 10 minutes. Groups rotate to debate each scenario with another group, noting new points on worksheets.

45 min·Small Groups

Dilemma Role-Play: Safety Scenarios

Assign pairs roles like doctor, patient, or AI developer in safety dilemmas, such as AI controlling emergency vehicles. Pairs act out 3-minute skits, then switch roles. Whole class votes on best resolutions and discusses outcomes.

35 min·Pairs

Guideline Workshop: School AI Rules

In small groups, students brainstorm and draft five ethical guidelines for school AI tools, like chatbots. Groups present to class for feedback. Class votes and refines into a shared poster.

40 min·Small Groups

Bias Hunt: Dataset Analysis

Provide printed datasets with biases. Individuals or pairs identify unfair patterns, then share in whole class discussion to propose fixes.

30 min·Individual

Real-World Connections

• Consider the use of AI in loan application processing. If the training data reflects historical lending discrimination, the AI might unfairly deny loans to certain groups, impacting their financial opportunities.
• Explore how AI is used in predictive policing. If the data used to train the system is biased, it could lead to over-policing in specific neighborhoods, raising serious fairness concerns.

Assessment Ideas

Discussion Prompt

Present students with a scenario: An AI recommends denying a student access to a specialized school program based on predicted future academic performance. Ask: Who is responsible if the AI is wrong? What information should the AI have access to? What information should it NOT have access to?

Quick Check

Provide students with a short paragraph describing an AI application (e.g., AI assisting doctors with diagnoses). Ask them to identify one potential ethical concern and one potential benefit, writing their answers on a sticky note.

Peer Assessment

Students work in small groups to draft one ethical guideline for AI use in schools. After drafting, groups swap their guideline with another group. Each group provides feedback on clarity and feasibility, suggesting one improvement.

Frequently Asked Questions

What ethical dilemmas arise from AI in health decisions?

AI in health can misdiagnose due to biased data, overlook rare conditions, or invade privacy through constant monitoring. Dilemmas include who takes responsibility for errors and whether patients trust machines over doctors. Classroom debates on these help students weigh innovation against risks, developing balanced views on regulation needs.

How can active learning help teach AI ethics in Year 6?

Active methods like role-plays and group debates make ethics tangible by letting students embody stakeholders and negotiate solutions. This builds empathy as they hear peers' concerns, while collaborative guideline design teaches consensus. Hands-on tasks outperform lectures, as pupils retain more through personal investment and real-time feedback.

How to address AI bias in primary computing lessons?

Show simple examples of biased datasets, like facial recognition failing on diverse skin tones. Students sort data in pairs to find imbalances, then redesign fairer sets. This links to online safety by stressing responsible data use, aligning with curriculum goals for critical digital literacy.

What guidelines should schools set for AI use?

Guidelines might require human oversight for decisions, transparent AI explanations, bias audits, and student data consent. Involve pupils in drafting via workshops to ensure buy-in. This promotes safe innovation, tying to KS2 standards on digital literacy and preparing children for ethical tech citizenship.