Digital Ethics Reflection Cards

What are dark patterns? Design choices that trick users into doing things they didn't intend to do.

Common examples:

• Hidden costs revealed at checkout
• Confusing unsubscribe processes
• Disguised advertisements
• Forced continuity (hard to cancel)
• Bait and switch tactics

Reflection: Have you encountered dark patterns recently? How did they make you feel? What would ethical alternatives look like?

Real consent requires:

• Information: Clear explanation of what data is collected
• Comprehension: Understandable language, not legal jargon
• Voluntariness: Real choice without coercion
• Competence: Ability to make informed decisions
• Agreement: Explicit action, not implied consent

Questions:

• Do users truly understand what they're agreeing to?
• Is declining consent as easy as accepting?
• Can they change their mind later?

Algorithms can perpetuate and amplify human biases:

Sources of bias:

• Biased training data
• Unrepresentative datasets
• Proxy discrimination
• Feedback loops reinforcing bias
• Designer assumptions and blind spots

Real-world impacts: Hiring algorithms, credit scoring, criminal justice, healthcare allocation, content moderation.

Action: How can we audit algorithms for fairness? Who should be involved in this process?

When products are free, you are the product.

Attention capture techniques:

• Infinite scroll
• Auto-play videos
• Notification bombardment
• FOMO triggers ("Someone viewed your profile")
• Streak counters and points
• Variable reward schedules

Questions to consider:

• What is the true cost of these "free" services?
• How do business models shape design decisions?
• What would attention-respecting design look like?

Accessibility isn't just compliance—it's about human dignity and inclusion.

Consider diverse needs:

• Visual impairments (blindness, low vision, color blindness)
• Hearing impairments
• Motor disabilities
• Cognitive differences
• Situational limitations (bright sunlight, noisy environments)
• Aging-related changes

Benefits for everyone: Clear language, good contrast, keyboard navigation, captions on videos.

Accessible design is better design.

The best way to protect data is to not collect it in the first place.

Key principles:

1. Purpose limitation: Collect data only for specific purposes
2. Adequacy: Ensure data is sufficient for the purpose
3. Necessity: Only collect what's actually needed
4. Retention limits: Delete data when no longer needed

Challenge: For each data point you collect, ask: "Do we really need this? What harm could occur if this data was breached?"

Design can support or harm mental health and wellbeing.

Design for wellbeing:

• Respect for time and boundaries
• Support for breaks and disconnection
• Mindful notification design
• Avoiding comparison metrics
• Promoting real connections over metrics
• Transparent about time spent

Red flags: Endless engagement, social pressure mechanics, anxiety-inducing features, sleep disruption.

How can we design technology that makes people feel better, not worse?

As AI becomes more prevalent, ethical considerations become critical.

Key questions:

• Transparency: Can people understand how decisions are made?
• Accountability: Who is responsible when AI makes mistakes?
• Human autonomy: Does AI enhance or replace human judgment?
• Fairness: Does the system treat all groups equitably?
• Privacy: What data is needed and how is it protected?

Human in the loop: Critical decisions should always involve human oversight.