Description
Overview
This workplace discrimination pattern spotting automation workflow leverages a no-code integration pipeline to collect and analyze employee review data for bias detection. Designed for HR analysts and diversity officers, it uses a manual trigger node to initiate data gathering from Glassdoor via ScrapingBee, then applies AI-driven demographic analysis for deterministic identification of disparities.
Key Benefits
- Automates extraction of company review data and demographic distributions from Glassdoor using HTTP and HTML parsing.
- Calculates statistical measures including variance, standard deviation, z-scores, and effect sizes for workplace bias detection.
- Integrates AI-based natural language processing to convert raw HTML reviews into structured numerical insights.
- Generates visual data representations with scatterplots and bar charts for clear demographic disparity visualization.
Product Overview
This workplace discrimination pattern spotting workflow begins with a manual trigger, requiring user initiation to start the process. The company name is predefined but can be configured for any target organization. The workflow uses ScrapingBee API nodes with HTTP query authentication to scrape Glassdoor search results, extract company page URLs, and then retrieve detailed employee reviews and demographic modules. HTML extraction nodes parse key content areas such as overall ratings and demographic-specific review segments.
The core logic involves OpenAI Chat Model nodes that transform extracted HTML into numerical data for average ratings, review counts, and star distribution percentages. Following this, the workflow calculates statistical variance and standard deviation from rating distributions. Z-scores and effect sizes are computed for each demographic group, considering sample size and rating differences, enabling quantitative assessment of workplace equity. A custom JavaScript node calculates p-values based on z-scores to evaluate statistical significance.
Visualization nodes format demographic statistical data into datasets for QuickChart API, which produces scatterplots and bar charts depicting demographic disparities in employee experience. The workflow operates synchronously on a manual trigger basis, with no persistent data storage beyond transient processing during execution. Error handling relies on platform defaults without explicit retry or backoff mechanisms.
Features and Outcomes
Core Automation
The automation workflow processes employee review data inputs to detect workplace discrimination using statistical thresholds and demographic segmentation. Nodes include manual trigger, data extraction, AI-powered information extraction, and statistical computation.
- Executes single-pass evaluation of demographic review ratings against overall company averages.
- Applies sample size weighting in z-score calculations to ensure statistical validity.
- Automatically removes demographic groups with no review data from statistical outputs.
Integrations and Intake
The orchestration pipeline integrates multiple APIs and services for data intake: ScrapingBee for web scraping with HTTP query authentication, OpenAI for AI-based text extraction, and QuickChart for visualization. The workflow requires predefined company names and demographic keys formatted as strings.
- ScrapingBee API: gathers raw HTML content from Glassdoor search and review pages.
- OpenAI Chat Models: extract structured rating and demographic data from HTML segments.
- QuickChart API: generates visual scatterplot and bar chart images based on computed metrics.
Outputs and Consumption
The no-code integration produces structured numerical outputs including average ratings, review counts, star rating distributions, z-scores, effect sizes, and p-values for each demographic group. Visual outputs are synchronous HTTP responses in PNG format representing scatterplots and bar charts.
- Numerical data keyed by demographic groups (e.g., asian_average_rating, female_total_number_of_reviews).
- Scatterplot dataset with x-axis as z-scores and y-axis as effect sizes for visual bias analysis.
- Bar charts displaying effect size magnitudes per demographic group for comparative review.
Workflow — End-to-End Execution
Step 1: Trigger
The workflow initiates manually via the manualTrigger node named “When clicking ‘Test workflow’”. This design requires explicit user action to start data collection and analysis, allowing controlled execution and testing.
Step 2: Processing
Initial processing sets the target company name and a dictionary of demographic keys defining groups for analysis. The workflow then performs multiple HTTP requests to ScrapingBee, authenticating with HTTP query credentials to retrieve Glassdoor search results and company review pages. HTML extraction nodes parse relevant URL paths and review content. Basic presence checks ensure required fields such as URLs and HTML segments are available before passing data forward.
Step 3: Analysis
The workflow uses OpenAI Chat Models to extract structured numerical data on overall ratings and demographic-specific reviews from HTML content. Statistical nodes calculate variance contributions per star rating distribution, summing these to derive variance and standard deviation. Z-scores and effect sizes are computed per demographic group using formulas that account for sample size and rating deviations. A JavaScript code node calculates two-tailed p-values for each z-score, removing groups without review data from statistical outputs.
Step 4: Delivery
Final data formatting prepares datasets for QuickChart API calls that generate scatterplot and bar chart images depicting demographic disparities. These images are returned synchronously via HTTP requests. Additionally, an OpenAI node performs text analysis to summarize key takeaways and contextualize employee experiences based on statistical results.
Use Cases
Scenario 1
HR teams need to identify subtle workplace discrimination patterns from employee feedback. This workflow automates data extraction and statistical analysis of demographic review ratings. The result is a clear, data-driven view of potential inequities, enabling targeted diversity interventions.
Scenario 2
Diversity officers require regular monitoring of company sentiment across demographic groups. The orchestration pipeline produces up-to-date statistical metrics and visualizations from Glassdoor reviews, facilitating ongoing equity assessments without manual data processing.
Scenario 3
Data analysts seek to quantify the significance of workplace experience differences among employee groups. This no-code integration calculates z-scores, effect sizes, and p-values, providing statistically grounded insights to inform organizational policy and culture improvements.
How to use
To deploy this workflow, import it into your n8n environment and configure the ScrapingBee and OpenAI API credentials with valid authentication tokens. Edit the “SET company_name” node to specify the organization under analysis. Upon manual trigger, the workflow scrapes Glassdoor for review data, extracts structured metrics, computes statistical analyses, and generates visual charts. The output includes both numerical datasets and chart URLs for immediate review. Expect structured JSON outputs with demographic ratings, statistical measures, and URL links to generated visualizations for integration into reporting tools.
Comparison — Manual Process vs. Automation Workflow
| Attribute | Manual/Alternative | This Workflow |
|---|---|---|
| Steps required | Multiple manual data collection, parsing, and statistical calculation steps | Single-trigger end-to-end automated pipeline with integrated data extraction and analysis |
| Consistency | Subject to human error and inconsistent data processing | Deterministic, repeatable calculations with standardized AI extraction and formula application |
| Scalability | Limited by manual capacity and tool integration complexity | Scalable to multiple companies and demographics with minimal configuration changes |
| Maintenance | High, due to manual updates and error-prone data handling | Lower, centralized in workflow nodes with reusable components and credential management |
Technical Specifications
| Environment | n8n automation platform |
|---|---|
| Tools / APIs | ScrapingBee API, OpenAI Chat Models, QuickChart API |
| Execution Model | Manual trigger synchronous execution |
| Input Formats | Company name string, HTTP query parameters, HTML content |
| Output Formats | Structured JSON, PNG images for charts |
| Data Handling | Transient processing with no data persistence |
| Known Constraints | Dependent on Glassdoor data availability and ScrapingBee proxy service |
| Credentials | HTTP Query Authentication for ScrapingBee, OpenAI API Key |
Implementation Requirements
- Valid ScrapingBee API credentials with HTTP query authentication configured in n8n.
- OpenAI API key with access to Chat Model nodes for text extraction and analysis.
- Network access allowing outbound HTTP requests to Glassdoor via ScrapingBee and QuickChart APIs.
Configuration & Validation
- Verify ScrapingBee credentials by executing sample HTTP requests to Glassdoor URLs and confirming valid HTML response.
- Test OpenAI Chat Model nodes with sample HTML input to ensure correct extraction of numerical rating data.
- Run workflow manually to confirm end-to-end data flow, statistical calculations, and generation of chart URLs without errors.
Data Provenance
- Trigger node:
manualTriggerinitiates the workflow. - ScrapingBee nodes perform authenticated HTTP GET requests to Glassdoor search and review pages.
- OpenAI Chat Model nodes extract and structure rating distributions and demographic review data from HTML content.
FAQ
How is the workplace discrimination pattern spotting automation workflow triggered?
The workflow is initiated manually via a dedicated manual trigger node, requiring explicit user activation to start data gathering and analysis.
Which tools or models does the orchestration pipeline use?
The pipeline integrates ScrapingBee for web scraping, OpenAI Chat Models for AI-driven data extraction, and QuickChart for generating data visualizations.
What does the response look like for client consumption?
Outputs include structured JSON with demographic ratings, statistical metrics such as z-scores and effect sizes, and URLs to PNG charts representing the data visually.
Is any data persisted by the workflow?
No data is persisted; all processing occurs transiently within the workflow execution without storage beyond runtime.
How are errors handled in this integration flow?
Error handling relies on n8n platform defaults; no custom retry or backoff logic is implemented within the workflow nodes.
Conclusion
This workplace discrimination pattern spotting workflow automates the collection, statistical analysis, and visualization of employee review data to identify demographic disparities. It delivers deterministic outcomes such as z-scores, effect sizes, and p-values to quantify workplace equity for diverse groups. While reliant on external API availability from Glassdoor via ScrapingBee, OpenAI, and QuickChart, the workflow provides a replicable, no-code integration solution for HR analytics. Its design enables consistent, scalable detection of potential discrimination patterns without manual data handling, supporting informed diversity and inclusion assessments.
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