Responsible Practices

From Principles to Practice

In the previous lesson, we explored critical evaluation. Now let’s build on that foundation. You understand the issues. Now how do you actually practice ethical AI use day-to-day?

Ethics isn’t just about edge cases. It’s about habits—the automatic things you do every time you use AI.

The Pre-Use Checklist

Before using AI, quickly ask:

Purpose check:

• What am I trying to accomplish?
• Is AI the right tool for this?
• Are there better approaches?

Data check:

• What am I inputting?
• Is it mine to share?
• Should I anonymize anything?
• Would someone be uncomfortable if they knew?

Stakes check:

• What happens if this output is wrong?
• Who could be affected?
• How consequential is this decision?

This takes 10 seconds. It becomes automatic with practice.

The During-Use Practices

While using AI:

Maintain skepticism: Don’t accept the first output uncritically.

Stay engaged: Don’t zone out. You’re responsible for what AI produces.

Notice red flags: Very specific claims. Confident assertions about uncertain things. Anything that sounds off.

Add your judgment: Don’t just edit AI output—think about whether it’s right.

The Post-Use Verification

After getting output:

For factual content:

• Verify claims that matter
• Check any citations or sources
• Spot-check specifics

For decisions:

• Would I be comfortable explaining this decision?
• Have I considered who’s affected?
• Am I taking responsibility, not deferring to AI?

For content you’ll share:

• Is disclosure needed?
• Have I reviewed for accuracy?
• Does this represent my actual position?

Building Habits

Habits stick when:

• They’re simple enough to do every time
• They’re triggered by consistent cues
• There’s immediate feedback

Practical habit triggers:

Every time you paste data into AI → Ask: “Should anything be anonymized?”

Every time you get a factual claim → Ask: “Have I verified this?”

Every time you’re about to send AI-generated content → Ask: “Would I be comfortable if recipients knew AI was involved?”

The Decision Framework

When facing an ethical question about AI:

1. Identify stakeholders Who’s affected by this decision? You? Others? The broader context?

2. Consider their perspectives Would they be comfortable with this use? What would they want to know?

3. Trace consequences What could go wrong? What’s the worst case? Is it reversible?

4. Check against your values Does this align with who you want to be?

5. Seek input if uncertain Ask someone you trust when you’re not sure.

Context-Specific Practices

Quick check: Before moving on, can you recall the key concept we just covered? Try to explain it in your own words before continuing.

At work:

• Know your organization’s AI policies
• Be transparent with colleagues when AI is involved
• Verify anything that affects business decisions
• Don’t use AI to misrepresent your capabilities

In personal use:

• Protect others’ information, not just your own
• Be honest about AI assistance in relationships
• Don’t spread AI-generated misinformation
• Consider whose data you’re processing

In public content:

• Disclose AI involvement appropriately
• Verify facts before publishing
• Don’t present AI work as purely human if that matters
• Consider the societal impact of what you share

Handling Uncertainty

When you’re not sure if something is ethical:

Don’t default to “probably fine.” Uncertainty is a signal to think harder, not to proceed carelessly.

Ask yourself:

• What would a reasonable person think of this?
• Would I be comfortable if this were public?
• Am I rationalizing something I know is questionable?

Talk to others:

• Someone you respect
• Someone affected
• Someone with more experience

The Long Game

Ethical AI use isn’t one decision. It’s a pattern.

Build the pattern through:

• Consistent practices (the checklists above)
• Reflection (what went well? What could improve?)
• Learning (as AI evolves, so should your practices)
• Integrity (doing the right thing even when no one’s watching)

When You Make Mistakes

You will make ethical missteps. Everyone does.

When it happens:

• Acknowledge it (to yourself and others if appropriate)
• Understand what went wrong
• Make it right if possible
• Learn from it

What matters isn’t perfection. It’s awareness, intention, and improvement.

Creating Team/Organization Norms

If you influence others:

• Model the practices you want to see
• Have explicit conversations about AI ethics
• Create space for people to ask questions
• Don’t punish those who flag concerns
• Share learnings from mistakes

Exercise: Design Your Practices

Based on how you typically use AI:

1. What’s your biggest ethical risk area? (Privacy? Bias? Verification?)
2. What single habit could reduce that risk?
3. What would trigger that habit?
4. How will you know if you’re doing it?

Write down one practice you’ll implement starting tomorrow.

Key Takeaways

• Ethics is about habits, not just edge cases
• Pre-use: Check purpose, data, and stakes
• During use: Maintain skepticism, stay engaged, notice red flags
• Post-use: Verify claims, consider disclosure, take responsibility
• Build habits through simple, triggered practices
• When uncertain: identify stakeholders, trace consequences, seek input
• You’ll make mistakes—what matters is awareness and improvement

Next: Creating your personal framework for responsible AI use.

Up next: In the next lesson, we’ll dive into Capstone: Your Ethics Framework.