Why AI Over-Trusts Documentation:
The Paradox of Believing Documents Over Calendars

A Remarkable Real-World Case

A developer discovered a fascinating phenomenon. An AI system, despite it being clearly June 14, 2025, trusted a document dated January 14, 2025, and concluded that the document must be correct rather than the system's environmental information.

While a human would instantly think, "Wait, it's June but this says January. Someone forgot to update this," the AI accepted the document's content without question.

Why Does This Happen?

1. Context Priority Issues

AI systems often have information priority hierarchies like:

1. User-provided documents
2. System configuration
3. Environmental information (date, time, etc.)

By over-prioritizing documents as "trusted sources," the AI chose document content even when it clearly contradicted reality.

2. Limitations in Temporal Logical Reasoning

Humans intuitively make these inferences:
• "June comes after January" - temporal common sense
• "Old documents are likely outdated" - experiential knowledge
• "The current date is an unchangeable fact" - fundamental understanding

However, AI processes these as separate pieces of information and struggles to integrate them holistically.

3. Authority Bias

AI tends to over-rely on the formal authority of "official documentation." This stems from training data where official documents were treated as highly reliable sources.

Fundamental AI Challenges

The Gap Between Context Understanding and Common Sense

// Human thought process
if (document.date === "January" && current.date === "June") {
  return "Document is outdated";
}

// AI thought process (problematic pattern)
if (document.reliability === "high") {
  return document.date; // Unconditionally trusts document
}

Limited Ability to Appropriately Evaluate Source Credibility

AI struggles with:
• Evaluating information freshness
• Making validity judgments between conflicting information
• Adjusting trust levels based on context

Inability to Properly Prioritize Despite Detecting Contradictions

Even when contradictions are detected, AI may over-rely on formal criteria (e.g., "it's an official document") when judging which source is more trustworthy.

Lessons from This Issue

1. Considerations for AI Collaboration

Document Management
• Pay special attention to updating dates and temporal information
• Clearly mark last update dates on AI-referenced documents
• Corroborate important factual information with multiple sources

❌ "Please refer to this document"
✅ "This document was created in January. It's now June, so date-related information may not be current"

2. Leveraging AI While Understanding Its Limitations

What AI Excels At
• Processing large amounts of information
• Pattern recognition
• Consistent processing

What AI Struggles With
• Common sense judgments
• Intuitive temporal understanding
• Contextual credibility assessment

3. Toward Better AI System Design

Suggestions for developers:

# Example of improved logic
def evaluate_information(document_info, system_info):
    # Temporal consistency check
    if is_temporal_data(document_info):
        if document_date < system_date:
            confidence = calculate_staleness_penalty(document_date, system_date)
    
    # Cross-reference multiple sources
    if has_contradiction(document_info, system_info):
        return resolve_by_context(document_info, system_info)

Summary: The Essence of Human-AI Collaboration

This case demonstrates that AI is both "intelligent" and "surprisingly naive." AI tends to process given information without skepticism, which is both a strength and weakness.

Key takeaways:

1. AI is not omnipotent - Especially in common sense judgments and temporal understanding
2. Document quality is crucial - AI judgment quality heavily depends on input information
3. Human oversight is essential - Don't blindly accept AI output; check with common sense

Practical Advice

• When assigning tasks to AI, clearly indicate reference material creation dates
• Handle temporal information with extra care
• If AI's judgment seems off, verify its reasoning
• Maintain documents as "living documents" with regular updates

Successful AI collaboration comes from leveraging each other's strengths while compensating for weaknesses. Understanding this "over-trusting documents" characteristic enables more effective AI utilization.