n8n workflow template · 26 nodes · OpenAI
Spot Workplace Discrimination Patterns with AI is a free n8n workflow template that starts when you run it manually in the n8n editor and works with OpenAI and QuickChart. It has 26 nodes, 6 of them AI nodes (OpenAI Chat Model, Information Extractor and Basic LLM Chain). To run it, you add your own credentials for HTTP Query Auth and OpenAI API.
The text below comes from the sticky notes inside the workflow. We removed links, code samples and anything that looks like a key or token.
Wes performed the initial data analysis highlighting problematic behaviors at Twilio. I wanted to try and democratize the data analysis they performed for those less technical.
Instead, ScrapingBee offers a great tool with a very generous package of free tokens per month, which works out to roughly 4-5 runs of this workflow.
Z-Score – A statistical measure that indicates how many standard deviations a data point is from the mean. In this analysis, a negative z-score suggests a group rates their workplace experience lower than the average, while a positive z-score suggests a better-than-average experience.
Effect Size – A measure of the magnitude of difference between groups. Larger negative effect sizes indicate a more substantial disparity in workplace experiences for certain groups, making it useful for identifying meaningful gaps beyond just statistical significance.
P-Score (P-Value) – The probability that the observed differences occurred by chance. A lower p-score (typically below 0.05) suggests the difference is statistically significant and unlikely to be random. In this analysis, high p-scores confirm that the disparities in ratings for marginalized groups are unlikely to be due to chance alone.
These metrics help quantify workplace disparities among demographic groups. Z-scores show which groups report better or worse experiences, effect sizes reveal the severity of these differences, and p-scores confirm whether the disparities are statistically meaningful. This data allows for a more informed discussion about workplace equity and areas needing improvement.
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Disabled employees reported the lowest average ratings, with a z-score of -5.50, indicating a far worse experience compared to their non-disabled peers.
Members of the LGBTQIA community showed significantly lower ratings (z-score of -2.53), suggesting they may experience a workplace environment that is less inclusive or supportive compared to others.
Transgender employees rated their experiences considerably lower (z-score of -2.91), highlighting a critical area for improvement in workplace culture and acceptance.
In contrast, veterans had the highest ratings (z-score of 1.54), which could indicate a supportive environment or programs tailored to their needs.
A noticeable gap exists in average ratings by gender, with female employees scoring below male employees, suggesting potential gender biases or challenges in workplace dynamics.
For members of groups reporting significantly worse experiences, such as disabled, transgender, and LGBTQIA employees, the workplace may feel alienating or unwelcoming. These individuals might perceive that their contributions are undervalued or overlooked and that necessary support systems are lacking, creating a culture of exclusion rather than one of inclusivity. This feeling of being marginalized can lead to poorer engagement, higher turnover rates, and diminished overall job satisfaction, adversely impacting both employees and the organization.
The text below comes from the sticky notes inside the workflow. We removed links, code samples and anything that looks like a key or token.
The 26 nodes in file order (sticky notes excluded). Branches and connections determine execution order in n8n:
Configure your own credentials after importing. Create each credential in n8n, then select it in the node.
Do you need somewhere to run it? Read our guide to self-hosted n8n.
We can adapt this workflow to your apps, data and process, then deploy and support it.
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