Description
Use AI and statistics to analyze Glassdoor reviews and reveal hidden patterns of workplace bias or discrimination.
Ideal for researchers, journalists, DEI teams, and advocacy groups seeking evidence-based insights.
🔹 How It Works:
✅ 🕸 Scrape Reviews – Pull employee feedback from Glassdoor using ScrapingBee
✅ 📊 Demographic Extraction – Use OpenAI-powered text analysis to identify group-based mentions
✅ 📈 Statistical Analysis – Measure disparities using z-scores, p-values, and effect sizes
✅ 📉 Visual Reporting – Auto-generate charts (scatter, bar) to visualize biases and inequities
💼 Use Cases:
🧑⚖️ DEI Research – Back diversity and inclusion goals with quantitative insights
📰 Investigative Journalism – Validate claims of discrimination using data
🏢 HR & Compliance – Monitor internal reviews for risk and bias
📚 Academic Studies – Examine systemic patterns in workplace experiences
🧠 Why It’s Smart:
📋 Real-World Reviews – Uses authentic employee feedback
📊 Statistical Rigor – Calculates significance, not just surface-level metrics
🎯 Insightful Charts – Visualizations make findings clear and actionable
🔁 Repeatable Process – Easily switch companies and rerun the analysis
📣 Equity Advocacy – Supports transparency and systemic accountability
🔧 Level of Effort:
🟡 Medium – Requires API setup and one-time configuration (~20 minutes)
🧩 You’ll Need:
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🔐 ScrapingBee API Key (for Glassdoor scraping)
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🤖 OpenAI API Key (for text-based demographic extraction)
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🏢 Target company name (best results for large U.S. firms)
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📊 Jupyter or visualization-ready environment (optional but ideal)
💡 Customization Tip:
Swap in other review sources (e.g., Indeed, Blind) or plug results into your own dashboards (Metabase, Tableau, etc.) for deeper internal tracking and comparison.
🚀 Shine a Light on Workplace Disparities with AI and Data Science.
From text to truth — turn employee voices into actionable insights for a fairer future.
