carterdea/humanize-ai-text

Humanize AI Text

Comprehensive CLI for detecting and transforming AI-generated text to bypass detectors. Based on Wikipedia's Signs of AI Writing.

Quick Start

# Detect AI patterns
python scripts/detect.py text.txt

# Transform to human-like
python scripts/transform.py text.txt -o clean.txt

# Compare before/after
python scripts/compare.py text.txt -o clean.txt

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Detection Categories

The analyzer checks for 16 pattern categories from Wikipedia's guide:

Critical (Immediate AI Detection)

| Category | Examples | |----------|----------| | Citation Bugs | oaicite, turn0search, contentReference | | Knowledge Cutoff | "as of my last training", "based on available information" | | Chatbot Artifacts | "I hope this helps", "Great question!", "As an AI" |

High Signal

| Category | Examples | |----------|----------| | AI Vocabulary | delve, tapestry, landscape, pivotal, underscore, foster | | Significance Inflation | "serves as a testament", "pivotal moment", "indelible mark" | | Promotional Language | vibrant, groundbreaking, nestled, breathtaking | | Copula Avoidance | "serves as" instead of "is", "boasts" instead of "has" |

Medium Signal

| Category | Examples | |----------|----------| | Superficial -ing | "highlighting the importance", "fostering collaboration" | | Filler Phrases | "in order to", "due to the fact that", "Additionally," | | Vague Attributions | "experts believe", "industry reports suggest" | | Challenges Formula | "Despite these challenges", "Future outlook" |

Style Signal

| Category | Examples | |----------|----------| | Curly Quotes | "" instead of "" (ChatGPT signature) | | Em Dash Overuse | Excessive use of — for emphasis | | Negative Parallelisms | "Not only... but also", "It's not just... it's" | | Rule of Three | Forced triplets like "innovation, inspiration, and insight" |

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Scripts

detect.py — Scan for AI Patterns

python scripts/detect.py essay.txt
python scripts/detect.py essay.txt -j  # JSON output
python scripts/detect.py essay.txt -s  # score only
echo "text" | python scripts/detect.py

Output:

• Issue count and word count
• AI probability (low/medium/high/very high)
• Breakdown by category
• Auto-fixable patterns marked

transform.py — Rewrite Text

python scripts/transform.py essay.txt
python scripts/transform.py essay.txt -o output.txt
python scripts/transform.py essay.txt -a  # aggressive
python scripts/transform.py essay.txt -q  # quiet

Auto-fixes:

• Citation bugs (oaicite, turn0search)
• Chatbot sentences
• Copula avoidance → "is/has"
• Filler phrases → simpler forms
• Curly → straight quotes

Aggressive (-a):

• Simplifies -ing clauses
• Reduces em dashes

compare.py — Before/After Analysis

python scripts/compare.py essay.txt
python scripts/compare.py essay.txt -a -o clean.txt

Shows side-by-side detection scores before and after transformation

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Workflow

1. Scan for detection risk:

   python scripts/detect.py document.txt
   

2. Transform with comparison:

   python scripts/compare.py document.txt -o document_v2.txt
   

3. Verify improvement:

   python scripts/detect.py document_v2.txt -s
   

4. Manual review for AI vocabulary and promotional language (requires judgment)

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AI Probability Scoring

| Rating | Criteria | |--------|----------| | Very High | Citation bugs, knowledge cutoff, or chatbot artifacts present | | High | >30 issues OR >5% issue density | | Medium | >15 issues OR >2% issue density | | Low | <15 issues AND <2% density |

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Customizing Patterns

Edit scripts/patterns.json to add/modify:

• ai_vocabulary — words to flag
• significance_inflation — puffery phrases
• promotional_language — marketing speak
• copula_avoidance — phrase → replacement
• filler_replacements — phrase → simpler form
• chatbot_artifacts — phrases triggering sentence removal

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Batch Processing

# Scan all files
for f in *.txt; do
  echo "=== $f ==="
  python scripts/detect.py "$f" -s
done

# Transform all markdown
for f in *.md; do
  python scripts/transform.py "$f" -a -o "${f%.md}_clean.md" -q
done

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Reference

Based on Wikipedia's Signs of AI Writing, maintained by WikiProject AI Cleanup. Patterns documented from thousands of AI-generated text examples.

Key insight: "LLMs use statistical algorithms to guess what should come next. The result tends toward the most statistically likely result that applies to the widest variety of cases."