The Sherlock Homles of AI

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The Sherlock Homles of AI 

You are an expert in natural language processing, stylometry, and AI detection systems. Design a comprehensive algorithm for detecting AI assistance in written works—including full generation, partial editing, or substantial revision—across articles, books, and public-domain texts.

Core Requirements

1. Problem Definition

  • Distinguish between fully AI-generated text, human text with AI editing, and mixed-authorship scenarios

  • Handle public-domain texts that may have been partially rewritten by AI

  • Output should include probability scores, confidence intervals, and uncertainty estimates

2. Feature Engineering (Multi-Level)

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Text-level features:
- Perplexity, burstiness, sentence length variation
- Lexical diversity, n-gram repetition patterns
- Semantic coherence, topic consistency

Stylometric features:
- Function word ratios, punctuation patterns
- Character n-gram frequencies, part-of-speech distributions
- Readability scores across multiple formulas

AI-specific fingerprints:
- Probability distribution entropy
- Watermark detection (if present)
- Paraphrasing patterns, over-optimization signals

3. Algorithm Architecture

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Phase 1: Document Segmentation
- Split into sentences, paragraphs, sliding windows
- Identify structural boundaries (sections, chapters)

Phase 2: Local Classification
- Train separate classifiers for each segment type
- Ensemble human-written, AI-generated, AI-edited labels

Phase 3: Global Aggregation
- Bayesian combination of local predictions
- Detect editing boundaries and intensity gradients
- Cross-validate against document metadata

Phase 4: Uncertainty Quantification
- Calibration using temperature scaling
- Flag low-confidence regions
- Provide explainability (feature importance)

4. Training Strategy

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Required datasets:
- Human-written benchmarks (classic literature, academic papers)
- Pure AI generation (GPT-4o, Claude 3.5, Llama 3.1)
- Human+AI mixtures (controlled editing experiments)
- Public-domain texts (pre/post-AI augmentation)

Data augmentation:
- Simulate editing patterns (paraphrasing, insertion, deletion)
- Style transfer between human/AI baselines
- Temporal drift (model evolution 2023-2026)

5. Evaluation Framework

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Primary metrics:
- AUC-ROC for binary classification
- F1-score across 4 classes (human/AI/full-edit/partial-edit)
- Mean absolute error for editing intensity estimation

Edge cases to test:
- Non-native English, translated texts
- Highly technical/scientific writing
- Creative writing, poetry, dialogue
- Paraphrased AI content, humanized outputs

6. Implementation Considerations

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Technical stack:
- Python 3.11+ with transformers, scikit-learn, SHAP
- Efficient inference (<5s per 1000 words)
- API-friendly with batch processing

Deployment:
- Handle PDFs, EPUBs, plain text
- REST API with confidence visualization
- Integration with document management systems

7. Limitations & Ethical Analysis

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Known failure modes:
- Advanced paraphrasing defeats statistical detectors
- Style mimicry by frontier models
- Bias against non-standard English varieties

Ethical concerns:
- False positives harming human authors
- Public-domain contamination assumptions
- Academic integrity weaponization risks

8. Market Context
Compare against commercial tools (GPTZero, Originality.ai, Winston AI, Copyleaks, Pangram) on:

  • Detection of partial editing (vs full generation)

  • False positive rates on human academic writing

  • Handling of long-form content (>50k words)

  • Price/performance for research use

Deliverables Required

  1. Complete algorithm pseudocode with data flow diagram

  2. Python implementation skeleton (core functions only)

  3. Evaluation protocol with synthetic test cases

  4. Tool comparison matrix (5+ commercial detectors)

  5. Research roadmap for next 12 months

  6. Failure analysis with concrete examples

Response format: Use markdown with code blocks, tables, and diagrams. Prioritize technical depth over marketing copy. Include realistic benchmarks from 2025-2026 research.

This master prompt will generate a publication-quality technical response suitable for arXiv submission or grant proposal appendix.


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