A Data-Driven Study of Ethical Concerns using NLP and Reddit Discourse
This project investigates how the public perceives the integration of Artificial Intelligence (AI) in workplace recruitment and employee evaluation. By analyzing a dataset of 2,998 Reddit posts, specifically identifying 729 hiring-related discussions, the study explores emerging ethical gaps in fairness, transparency, and accountability.
Organizations increasingly rely on automated résumé screening, algorithmic candidate ranking, and AI-driven interview assessments. [cite_start]This project asks: How can organizations ensure fairness, transparency, and accountability when using AI for hiring and employee evaluation?. Understanding public sentiment offers critical insight into the ethical gaps that remain unresolved as these systems grow more influential.
The project follows a rigorous Data Science pipeline to extract insights from unstructured social media data:
- Data Collection: 2,998 Reddit posts related to workplace AI were collected and filtered for hiring-specific terms like "hire," "interview," "candidate," and "application".
- NLP Preprocessing: Applied text cleaning through tokenization, punctuation removal, stopword filtering, and lemmatization.
- Sentiment Analysis: Computed sentiment scores using VADER to categorize discussions into positive, negative, and neutral categories.
- Thematic Detection: Used ethical keywords—bias, fairness, transparency, privacy, and accountability—to identify relevant concerns.
- Statistical Validation: Performed a t-test to compare average sentiment between hiring and non-hiring discussions to determine distinct emotional tones.
- Topic Modeling: Applied Latent Dirichlet Allocation (LDA) to uncover thematic differences in user discussions.
- Transparency as a Priority: Transparency was identified as the most prevalent ethical concern, followed by fairness and privacy.
- Emotional Intensity: Hiring discussions were found to be statistically more personally and emotionally charged compared to general technical AI talk.
- Negative Sentiment Drivers: Discussions involving Accountability and Bias skew more negative, reflecting unresolved harms and skepticism.
- Thematic Divide: Topic modeling revealed that hiring posts revolve around personal career transitions and uncertainty, while non-hiring posts remain solution-focused and technical.
Figure 1: Mind map of ethical concerns in AI hiring systems, including fairness, transparency, and accountability.
Figure 2: Frequency of ethical keywords in hiring-related Reddit posts, showing Transparency as the leading concern.
Figure 3: Common themes in negative hiring posts highlight issues with "models," "managers," and "jobs".
Figure 4: Distribution of sentiment across all Reddit posts about AI in the workplace.
To fetch data, you must have a Reddit developer account:
- Visit Reddit App Preferences.
- Click "create app" or "create another app".
- Name:
AI_Hiring_Analysis(or your preferred name). - App Type: Select script.
- Redirect URI: Use
http://localhost:8080. - Click "create app" to get your Client ID (under the app name) and Client Secret.
Create a file named .env in your root directory. This project uses the python-dotenv library to keep your keys safe. Add your credentials like this:
REDDIT_CLIENT_ID=your_client_id_here
REDDIT_CLIENT_SECRET=your_client_secret_here
REDDIT_USER_AGENT=your_app_name_here
Open your terminal in VS Code and run the following command to install the required Python libraries:
pip install -r requirements.txt
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Open the ai_bias.ipynb notebook in VS Code or Jupyter.
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Ensure your kernel is set to your Python environment.
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Execute the cells in order to reproduce the data fetching, cleaning, and visualizations.