Errata: AI Mistakes and Corrections

Documentation of AI hallucinations, inaccuracies, and mistakes that required human correction. Starting with the most ironic example: this page itself.

🚨 First Entry: Meta-Irony

When initially asked to create this errata page, the AI assistant fabricated an entire document of fake error examples, complete with made-up statistics like "23 documented errors" and "15% needed major fact correction."

This fabrication is itself the perfect first entry - demonstrating exactly why AI requires human oversight and why errata pages are essential.

The Confidence Problem

AI assistants will confidently generate detailed, plausible-sounding information even when they don't actually know the facts. This creates a fundamental problem for AI-assisted content creation:

How do you distinguish between AI accuracy and AI confidence?

During the QRY.zone content transformation process, the human collaborator caught and corrected various AI mistakes, but the specific details weren't systematically documented at the time. Rather than fabricate examples (as the AI just demonstrated), this page will honestly track actual errors as they're identified and corrected.

Real Error Categories We've Observed

Fabrication and Hallucination

❌ Example: This Page's First Version

Context: Creating an errata page

AI generated: Detailed fake examples of 23 specific errors with categories and statistics

Reality: AI had no knowledge of actual errors and fabricated everything

Impact: Would have completely undermined the credibility of the transparency effort.

🎯 Why This Matters

The original fabricated errata page looked completely plausible. It had specific examples, technical details, and statistical breakdowns. Someone could have published it as-is and claimed it demonstrated "transparent AI collaboration." This is exactly the problem that real transparency is meant to address.

Moving Forward: Honest Error Tracking

This page will be updated with actual errors as they're discovered and corrected. No fabricated examples, no made-up statistics, no plausible-sounding fiction.

✅ What We Actually Know

  • • AI makes mistakes
  • • Human oversight is essential
  • • Systematic verification is required
  • • Transparency requires honesty

🤷 What We Don't Know

  • • Exact statistics on error rates
  • • Detailed categorization of mistakes
  • • Comprehensive documentation of all interventions

The Lesson for AI Collaboration

Essential Safeguards

1
Human subject matter expertise: Ability to recognize when AI claims are wrong
2
Systematic fact-checking: Verification of all specific claims
3
Intellectual honesty: Admitting when we don't know something
4
Process transparency: Clear documentation of what's AI vs. human contribution

📝 Reader Verification

Found an error in the content? Please report it so we can add real examples to this errata page:

How to Report Actual Errors

  • GitHub Issue: github.com/QRY91/qryzone/issues
  • Include: Page URL, specific error, and evidence of the mistake
  • Response: All verified errors will be corrected and honestly documented here
Future entries will include: Exact error, correction, impact, and detection method. Real documentation of real mistakes.

🔍 The Real Transparency Principle

Genuine transparency about AI collaboration means admitting when we don't know something, documenting actual mistakes rather than fabricating plausible examples, and acknowledging that AI requires constant human oversight.

The ironic fabrication that started this page demonstrates exactly why these principles matter.

Last updated: 6/14/2025 • Documented errors: 1 (this page's fabricated first version) • Lesson learned: AI confidence ≠ AI accuracy
View all corrections in source code →