Lesson 5: Ethical Design – When Amplification Produces Harm

• When does engagement-driven design become harmful?
• How do feedback loops amplify misinformation or harmful behavior?
• Who holds responsibility for algorithmic outcomes?
• Can platforms maximize engagement while minimizing harm?
• What trade-offs are unavoidable in sociotechnical systems?

Ethics
Misinformation
Manipulation
Addiction
Privacy
Data harvesting
Algorithmic bias
Moderation
Responsibility
Regulation
Trade-off
Harm

HS ETS1-2
Analyze a complex real-world problem and evaluate possible solutions.

Science and Engineering Practices:
Engaging in Argument from Evidence
Constructing Explanations
Developing and Using Models

Crosscutting Concepts:
Cause and Effect
Systems and System Models
Stability and Change

• Analyzing informational case studies
• Evaluating claims using textual evidence
• Writing structured argumentative responses
• Identifying cause-and-effect chains
• Synthesizing multiple sources into a coherent argument

Day 1 – Ethical Position Provocations

Students respond individually to structured agree/disagree statements drawn from your ethics prompt materials:

Algorithms do not influence my choices.
Social media apps are designed to be healthy for users.
Platforms should not be responsible for what users post.

Students write initial justifications before discussion.

Purpose:
Surface assumptions and create cognitive dissonance.

DOK: 2 – Express and justify position.

Day 2 – Case Study Investigation

Students are assigned real-world case studies involving:

Privacy breaches
Misinformation spread
Mental health impact
Addictive design features
Algorithmic radicalization

Students analyze:

What happened
What system feature contributed
How amplification played a role
Who benefited
Who was harmed

Students must explicitly connect their explanation to the feedback loop model from Segment 4.

Purpose:
Tie ethical harm directly to system mechanics.

DOK: 3 – Analyze cause-and-effect relationships.

Day 3 – System Mapping of Harm

Students diagram:

Design feature ? Engagement behavior ? Amplification ? Consequence

For example:

Autoplay ? Increased watch time ? Amplification boost ? Prolonged exposure

Students identify:

Is this harm accidental or predictable?

Purpose:
Shift from emotional reaction to structural analysis.

DOK: 3

Day 4 – Design Modification Challenge

Students propose one design modification to reduce harm while preserving engagement.

Examples:

Delay resharing of viral posts
Hide public like counts
Add friction before reposting
Adjust amplification weight for extreme content

Students must explain:

How their modification changes the feedback loop
What trade-off is introduced

Purpose:
Prepare students for ethical safeguard integration in the final project.

DOK: 4 – Propose system-level redesign.

Optional Day 5 – Structured Debate

Students debate:

Should platforms prioritize free expression or harm reduction?
Should government regulation intervene in algorithm design?

Students must cite case evidence and amplification logic.

Students analyze events that have shaped public discourse, elections, identity formation, and mental health trends.

They recognize that:

Digital systems influence beliefs, behavior, and well-being.
Algorithm design choices shape cultural narratives.

• Harmful outcomes are purely user-driven.
• Platforms are neutral tools.
• Regulation eliminates all harm.
• Engagement automatically equals social value.
• Ethical design and profitability are mutually exclusive.

• Provide structured case analysis templates.
• Offer guided cause-and-effect sentence stems.
• Allow verbal debate participation prior to written submission.
• Extension: Have students compare two case studies and identify shared system patterns.

Formative Assessments:

• Case study written analysis
• Cause-and-effect system diagram
• Ethical design modification proposal

Exit Ticket Prompt:

Choose one design feature discussed this week.
Explain how it can create both engagement and harm using the feedback loop model.

Evaluation Criteria:

Accuracy of system reasoning
Use of evidence from case studies
Clarity of cause-and-effect explanation
Recognition of trade-offs