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量化各地區 AI 採用程度 (AUI = 使用率 / 工作年齡人口比例)\n- **隱私保護機制** - 自動過濾低於閾值的數據 (\u003c 15 對話或 \u003c 5 使用者)\n- **O*NET 職業分類** - 自動分類 AI 使用任務類型\n- **協作模式識別** - 分析人機協作模式 (自動化 vs 增強)\n- **視覺化報告** - 自動生成圖表與分析報告\n- **雙語支援** - 中英文報告與視覺化\n- **TDD 開發** - 122 個測試案例，95.9% 覆蓋率\n\n### 🚀 快速開始\n\n```bash\n# 1) Clone 專案\ngit clone https://github.com/thc1006/taiwan-ai-usage-index.git\ncd taiwan-ai-usage-index\n\n# 2) 安裝相依套件\npython3 -m venv .venv \u0026\u0026 source .venv/bin/activate  # Linux/Mac\n# 或 Windows: python -m venv .venv \u0026\u0026 .venv\\Scripts\\activate\npip install -r requirements.txt\n\n# 3) 執行測試確認安裝\npytest -q\n\n# 4) 執行示範模式\npython -m src.metrics.aui --demo\n\n# 5) 生成圖表與報告\npython -m src.viz.figures\npython -m src.report.make_report\n```\n\n### 📊 資料處理管線\n\n```python\nfrom src.ingest import process_anthropic_data\nfrom src.labeling import classify_task_llm, classify_mode_llm\nfrom src.metrics import AUICalculator\n\n# 1. 資料擷取與篩選 (台灣同儕國家: TWN, SGP, KOR, JPN, HKG)\ndf = process_anthropic_data(\n    'data/raw/anthropic_open/conversations.csv',\n    'data/interim/open/taiwan_filtered.parquet'\n)\n\n# 2. 任務分類 (O*NET/SOC)\ndf['task_category'] = df['summary'].apply(classify_task_llm)\n\n# 3. 協作模式分類\ndf['collab_mode'] = df['summary'].apply(classify_mode_llm)\n\n# 4. 計算 AUI 指數\ncalculator = AUICalculator(min_conversations=15, min_users=5)\nresults = calculator.process_data(df)\ncalculator.save_results(results, 'output/aui_results.csv')\n```\n\n### 📁 專案結構\n\n```\ntaiwan-ai-usage-index/\n├── src/\n│   ├── ingest/          # 資料擷取模組 (CSV → Parquet)\n│   ├── labeling/        # 分類標註模組 (O*NET, 協作模式)\n│   ├── metrics/         # AUI 計算模組\n│   ├── viz/             # 視覺化模組 (Matplotlib/Seaborn)\n│   └── report/          # 報告生成模組 (Markdown/JSON)\n├── tests/               # 測試套件 (122 個測試)\n├── prompts/             # Few-shot 提示範本\n├── data/\n│   ├── raw/            # 原始資料 (不納入版控)\n│   ├── interim/        # 中間處理資料\n│   └── processed/      # 最終資料\n├── figures/            # 生成圖表\n├── report/             # 分析報告\n└── ci/                 # CI/CD 工作流程\n```\n\n---\n\n## English\n\n### 🎯 Features\n\n- **Regional AI Usage Index** - Quantify AI adoption across Taiwan regions\n- **Privacy Protection** - Auto-filter data below thresholds (\u003c15 conversations or \u003c5 users)\n- **O*NET Classification** - Automatic task categorization using occupational taxonomy\n- **Collaboration Mode Detection** - Analyze human-AI interaction patterns (automation vs augmentation)\n- **Visual Reports** - Auto-generated charts and analysis reports\n- **Bilingual Support** - Chinese and English reports\n- **TDD Development** - 122 test cases with 95.9% coverage\n\n### 🚀 Quick Start\n\n```bash\n# 1) Clone repository\ngit clone https://github.com/thc1006/taiwan-ai-usage-index.git\ncd taiwan-ai-usage-index\n\n# 2) Install dependencies\npython3 -m venv .venv \u0026\u0026 source .venv/bin/activate  # Linux/Mac\n# or Windows: python -m venv .venv \u0026\u0026 .venv\\Scripts\\activate\npip install -r requirements.txt\n\n# 3) Run tests to verify installation\npytest -q\n\n# 4) Run demo mode\npython -m src.metrics.aui --demo\n\n# 5) Generate visualizations and report\npython -m src.viz.figures\npython -m src.report.make_report\n```\n\n### 📈 AUI Calculation Method\n\n```\nAUI = (Regional AI Usage Rate / Regional Working-Age Population Ratio) × 100\n```\n\nUsage Tiers:\n- **High Usage**: AUI ≥ 100\n- **Medium Usage**: 50 ≤ AUI \u003c 100\n- **Low Usage**: AUI \u003c 50\n\n### 🔬 Research Applications\n\n- **Policy Research** - Understand AI adoption patterns for policy making\n- **Market Analysis** - Assess regional AI maturity for business strategy\n- **Academic Studies** - Research human-AI collaboration patterns\n- **Social Impact** - Analyze digital divide and technology adoption\n\n### 🤝 Contributing\n\nWe welcome contributions! Please follow TDD principles and ensure tests pass before submitting PRs.\n\n### 📄 License\n\nMIT License - see [LICENSE](LICENSE) file for details.\n\n### 📚 Citation\n\n```bibtex\n@software{taui2025,\n  title = {Taiwan AI Usage Index (TAUI)},\n  author = {THC1006},\n  year = {2025},\n  url = {https://github.com/thc1006/taiwan-ai-usage-index}\n}\n```\n\n### 📮 Contact\n\n- **Issues**: [GitHub Issues](https://github.com/thc1006/taiwan-ai-usage-index/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/thc1006/taiwan-ai-usage-index/discussions)\n\n---\n\n## Notes\n- This repo is scaffolding for research reproducibility. Replace/extend sample data with your own **de-identified** volunteer data or open data slices.\n- Avoid committing PII. The tests enforce privacy filtering behavior.\n\n## cite\n```\n@online{appelmccrorytamkin2025geoapi,\n\nauthor = {Ruth Appel and Peter McCrory and Alex Tamkin and Michael Stern and Miles McCain and Tyler Neylon],\n\ntitle = {Anthropic Economic Index report: Uneven geographic and enterprise AI adoption},\n\ndate = {2025-09-15},\n\nyear = {2025},\n\nurl = {www.anthropic.com/research/anthropic-economic-index-september-2025-report},\n\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthc1006%2Ftaiwan-ai-usage-index","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthc1006%2Ftaiwan-ai-usage-index","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthc1006%2Ftaiwan-ai-usage-index/lists"}