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Practice analyzing and responding to AI outputs. For each scenario, consider how you would Use, Evaluate, and Question the AI output before clicking "Next" to see our analysis.
AI Output Exercises
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Sales Forecast
"60% chance Product A sales will decline 15% in Q4"
Reflection Prompts:
How would you interpret this forecast? What does a "60% chance" really mean for planning purposes?
What additional context might you need to properly evaluate this prediction?
What organizational impacts should you consider before making decisions based on this forecast?
Answer Guide
USE: Sensemaking and Adaptability
Don't treat the forecast as a certainty. Instead, think about how it connects to your current knowledge:
Are marketing or customer behavior trends already pointing to a downturn?
Should you begin contingency planning (e.g., reducing production orders, adjusting marketing tactics)?
EVALUATE: Probabilistic Thinking and Practical Significance Awareness
60% is a probability — not a guarantee. It's "more likely than not," but a 40% chance it won't happen is significant.
Also, is a 15% decline practically important? For some products, it might be small noise; for others, a big deal.
QUESTION: Ethical Reasoning and System Thinking
If you reduce inventory drastically based on this prediction, could it impact suppliers, customers, or employee hours unnecessarily?
Are you making decisions that treat people fairly given the uncertainty?
Resume Screening
"92% culture fit score"
Reflection Prompts:
What might the AI be looking at to generate a "culture fit" score?
How reliable do you think this score is, and what biases might be embedded in it?
What are the ethical implications of using an AI score for "culture fit" in hiring?
Answer Guide
USE: Insight Identification
Recognize that "culture fit" is subjective. How does this AI-generated score relate to other indicators you value (skills, experience, team needs)?
Don't accept the score blindly — use it alongside human interviews and assessments.
EVALUATE: Trust Calibration and Bias Awareness
How was "culture fit" trained? Is it replicating a biased historical hiring pattern?
Is it overemphasizing shared background rather than diversity of thought?
QUESTION: Challenging Assumptions and Accountability Thinking
Is "culture fit" a fair, transparent, and appropriate metric?
Are hiring managers aware of AI's involvement, and are candidates treated equitably?
Fraud Alert
"Unusual $5,200 order at 3 AM"
Reflection Prompts:
What alternatives to immediate transaction blocking might be appropriate here?
How could you assess whether this alert is likely to be a false positive?
What broader consequences should be considered when implementing automated fraud detection?
Answer Guide
USE: Sensemaking and Adaptability
Consider legitimate reasons (customer traveling, time zone differences, gift purchases).
Instead of immediate blocking, maybe flag for human review or customer verification.
EVALUATE: Critical Thinking and Practical Significance
What is the false positive rate?
Is this pattern truly anomalous for this customer segment (e.g., young tech buyers)?
QUESTION: System Thinking and Accountability
How would a wrongly blocked transaction affect trust and customer loyalty?
Who is responsible if the AI unfairly penalizes certain groups?
Generated Email
"Draft refund rejection"
Reflection Prompts:
How might you adapt this AI-generated content to better fit your brand voice and customer service philosophy?
What should you check to ensure the content is accurate and appropriate?
What human elements might be missing from an AI-drafted customer service response?
Answer Guide
USE: Creative Thinking and Sensemaking
Does the tone match your customer service philosophy?
Could you soften the language or suggest alternative resolutions?
EVALUATE: Data Literacy and Critical Thinking
Was the AI trained on brand-appropriate communication examples?
Is there any misinformation (e.g., citing non-existent policies)?
QUESTION: Empathy and Socratic Questioning
Does the email risk inflaming the situation?
Would a human conversation be more appropriate?
Dynamic Pricing Recommendation
"Raise prices 8% in low-supply areas"
Reflection Prompts:
What alternative strategies might address the supply-demand imbalance besides simply raising prices?
What factors should you evaluate to determine if this recommendation is sound?
What ethical considerations and long-term consequences should be examined?
Answer Guide
USE: Insight Identification and Creative Thinking
Understand what the AI is highlighting: supply shortages + rising demand.
Think creatively: Could bundling products, offering loyalty rewards, or managing expectations be smarter than simply raising prices?
EVALUATE: Critical Thinking and Probabilistic Thinking
Is this truly based on real-time supply chain updates?
Does the model capture possible backlash or only look at revenue maximization?
QUESTION: Ethical Reasoning and System Thinking
Are you unfairly burdening vulnerable communities?
How might frequent dynamic pricing erode customer trust long-term?
Employee Attrition Risk Score
"78% attrition likelihood"
Reflection Prompts:
How might you use this information constructively rather than punitively?
What factors should you consider when determining how much to trust this prediction?
What ethical concerns arise when employees are scored and categorized by AI?
Answer Guide
USE: Sensemaking and Adaptability
Instead of labeling the employee, proactively engage.
Stay Interviews: Meet with the employee before problems emerge, ask about job satisfaction, career goals, and frustrations.
Retention Support: Tailor interventions (mentorship, new projects, flexibility) based on their needs.
EVALUATE: Critical Thinking and Trust Calibration
Was the prediction based only on tenure, recent survey negativity, or shallow patterns?
Does the 78% likelihood justify action — or simply heightened awareness and follow-up?
QUESTION: Accountability Thinking and Ethical Reasoning
Are we creating self-fulfilling prophecies by treating "high-risk" employees differently?
How are employees protected against bias or misinterpretation of these scores?
Exercises Completed!
Congratulations! You've completed all six exercises analyzing AI outputs using the U-E-Q framework. You've practiced applying critical thinking skills to various AI scenarios, from sales forecasts to employee analytics.
Remember to apply these skills in your real-world interactions with AI:
USE: How can you effectively interpret and apply the AI output?
EVALUATE: How reliable and valid is this AI output?
QUESTION: What broader implications and ethical considerations should be examined?
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