Spot Workplace Discrimination Patterns with AI

Workflow Name

Spot Workplace Discrimination Patterns with AI

Key Features and Highlights

This workflow leverages ScrapingBee to extract employee review data from Glassdoor and combines it with OpenAI’s language models for intelligent data analysis to identify patterns of discrimination and bias in the workplace. It employs statistical methods to calculate rating differences among various demographic groups—including Z-scores, effect sizes, and p-values—and uses QuickChart to generate intuitive visualizations of the analysis results. This enables users to gain deep insights into workplace diversity and inclusion status.

Core Problems Addressed

Traditional workplace diversity surveys often rely on labor-intensive manual data collection and analysis, making it difficult to quickly uncover potential discrimination patterns. This workflow automates the extraction of publicly available review data and integrates AI-powered data extraction with statistical analysis to effectively reveal differences in workplace experiences across demographic groups, helping organizations identify and address potential inequities.

Application Scenarios

• HR departments monitoring and evaluating internal diversity and inclusion metrics
• Research institutions or consulting firms conducting workplace equity analyses
• Corporate culture development and employee satisfaction improvement initiatives
• Data support for social responsibility reporting and compliance audits

Main Process Steps

1. Manually trigger the workflow start.
2. Set the target company name (default example: Twilio).
3. Define a demographic group dictionary (e.g., race, gender, identity categories).
4. Use ScrapingBee proxy service to scrape HTML content from the company’s Glassdoor homepage and employee review pages.
5. Extract overall review summaries and segmented review data for each demographic group.
6. Parse and extract specific rating data and distribution proportions using OpenAI language models.
7. Calculate variance and standard deviation of ratings, then derive Z-scores, effect sizes, and p-values for each group.
8. Format the statistical results to generate scatter plot and bar chart configurations compatible with QuickChart.
9. Call the QuickChart API to generate charts that visually display differences in workplace experiences among groups.
10. Finally, the OpenAI model generates key insights and textual reports interpreting employee experience based on the analyzed data.

Involved Systems and Services

• ScrapingBee (web data proxy scraping service)
• Glassdoor (employee review data source)
• OpenAI (intelligent data extraction and text analysis)
• QuickChart (chart generation API)
• n8n automation platform (workflow execution and node management)

Target Users and Value

• HR analysts and diversity managers: Quickly identify workplace discrimination and bias to support decision-making.
• Corporate leadership: Understand employee diversity experiences to promote a fair and inclusive corporate culture.
• Data scientists and researchers: Obtain structured workplace diversity data for in-depth studies.
• Consulting professionals: Provide clients with data-driven workplace equity analysis reports.
• Social activists and policymakers: Monitor corporate diversity performance and advocate for workplace equity policies.

By using this workflow, users can automatically and intelligently uncover potential discrimination patterns within organizations, leveraging data-driven insights to foster a fairer and more inclusive work environment.