Uncovering Biases Based on Gender in Job Descriptions

AI detects hidden gender bias in job descriptions using NLP to flag biased terms, measure inclusivity, and promote fair, inclusive hiring.

The Problem

Imagine a classroom where only a few students are allowed to speak while others remain silent. This scenario mirrors what happens when certain voices dominate a dataset. When data lacks diversity, biases naturally emerge, leaving some groups underrepresented and unheard.

These biases raise important questions: Who is missing from this dataset? What perspectives are being ignored?

In the world of employment, gender bias continues to create barriers, limiting equality and progress toward an inclusive workforce. Despite growing awareness, bias often hides in subtle ways — embedded in job descriptions, corporate language, and recruitment materials.

Detecting and addressing these hidden biases is essential. By removing gender bias from job descriptions, companies can open the door to equal opportunities for everyone, regardless of gender identity.

The Goal

To build a fair and inclusive job market, we first need to recognize where gender bias exists. Once we identify these biases, we can take meaningful steps to remove them. Only then can companies truly hire based on merit rather than perception.

Our team decided to develop a Gender Bias Detection tool that analyzes job descriptions and identifies bias toward any gender. The goal was to help organizations understand how their language may unintentionally favor one gender over another.

By using this system, companies can evaluate and improve their hiring content, ensuring their recruitment processes are more transparent and equitable. This approach not only promotes inclusion but also helps businesses attract the most qualified candidates — regardless of gender identity.

The Background

A job listing is often the first point of contact between a potential employee and an organization. It shapes how candidates view both the company and the position they are considering. When bias exists in these listings, even subtly, it can discourage qualified candidates from applying or make them question whether they belong in that environment.

Gender bias is particularly common in job descriptions. During our research, we noticed that listings for jobs traditionally considered “female-oriented,” such as nurse, secretary, or typist, use very different language compared to listings for executive or managerial roles. This language difference often reinforces stereotypes, making leadership roles appear more suitable for men, even when the job itself is gender-neutral.

It is also important to understand that equality and business success are not mutually exclusive. Inclusive hiring not only expands opportunities for candidates but also helps organizations discover top talent that might otherwise be overlooked. By focusing on skills and merit, companies can build stronger, more innovative teams that drive long-term growth.

Our Approach: Building a system to ensure equitable hiring

Using techniques from Natural Language Processing (NLP)

We used Natural Language Processing (NLP) techniques to detect gender bias in job descriptions. NLP allows computers to analyze text and uncover patterns that may unintentionally favor one gender over another.

Techniques such as word embeddings and contextual embeddings help reveal the relationships between words and phrases. For example, models like Word2Vec and GloVe represent words in a vector space where similar meanings appear close together. By examining how gendered terms such as he or she relate to professional words like leader or manager, NLP models can identify hidden bias.

Modern transformer-based models, including BERT and GPT, build on this by understanding how words change meaning depending on context. This allows them to detect more subtle forms of bias that traditional models might miss.

Word Embeddings

Word embeddings are numerical representations of words that help computers understand their meanings and relationships. Techniques like Word2Vec and GloVe create these embeddings by analyzing how words appear together in large amounts of text.

When applied to job descriptions, these embeddings reveal how gender-specific terms relate to professional words. For example, if male or female pronouns frequently appear near terms like leader or manager, it can indicate an underlying bias.

To measure this, we compare the similarity between word vectors using methods such as cosine similarity or Euclidean distance. This helps the model identify patterns showing whether a job description leans toward one gender more than another.

Contextual Embeddings

Unlike traditional embeddings, contextual embeddings interpret a word’s meaning based on the surrounding text. Models such as BERT and GPT use attention mechanisms to identify which words carry the most significance within a sentence. This helps the model detect subtle gender bias that depends on phrasing and tone.

By fine-tuning these pre-trained models on annotated datasets, they learn to recognize biased language patterns more accurately. Techniques such as binary classification and sequence labeling further help the system identify and tag specific phrases or terms that contribute to bias.

Preparing the Dataset

Building a reliable gender bias detection model starts with a well-prepared dataset. The process involves collecting job descriptions from a variety of industries and roles, then labeling them for biased language, stereotypes, or unbalanced representation.

Human annotators review these texts and mark instances where bias appears. This labeled data becomes the foundation for training the model. To make sure the system performs well in real-world situations, the dataset must include diverse writing styles, job types, and industries. This diversity helps the model generalize better and avoid overfitting to a single pattern of language.

In one of the projects, we did the initial exploratory research shown below:

Engineering Features

Feature engineering transforms text into numerical data that machine learning models can process. In this stage, we extract features such as word embeddings, part-of-speech tags, syntactic patterns, and semantic relationships to help the model understand how words function within a job description.

We also include domain-specific features like job titles, company summaries, and required qualifications. These elements improve the model’s ability to detect context-specific bias.

Once the features are extracted, the model’s accuracy is evaluated using a confusion matrix, which shows how effectively it distinguishes between gender-neutral and biased text.

Selecting the Model

Different machine learning algorithms can be used depending on the dataset and the task. For gender bias detection, we experimented with logistic regression, support vector machines (SVMs), neural networks, and Conditional Random Fields (CRF).

The dataset is divided into training, validation, and testing sets. The training data helps the model learn to recognize bias, while the validation set ensures that it performs well on unseen examples. This process prevents overfitting and improves reliability. When labeled data is limited, pre-trained models such as BERT or GPT can be fine-tuned for bias detection. These models already understand general language patterns, which allows them to identify subtle gender bias even with smaller datasets.

Evaluating Model Performance

To ensure the model performs reliably, we use metrics such as precision, recall, and F1-score. These metrics help measure how accurately the model identifies gender bias and how well it avoids false positives or negatives. We also apply cross-validation to test the model’s stability across different data samples. This ensures that the model’s performance is consistent and not dependent on a single dataset.

In addition, we use the Gender Bias Index to quantify inclusivity levels in job descriptions. This index assigns a score based on how often gendered language appears and in what context. By tracking these scores over time, organizations can measure their progress toward creating more inclusive hiring content.

Utilizing the Power of Human Oversight and Feedback Loops

Even with advanced AI systems, human oversight remains essential. Creating feedback loops where diverse teams review flagged job descriptions ensures that both conscious and unconscious biases are identified and corrected. This collaborative review process adds a human perspective that AI alone cannot provide. It helps validate model outputs and ensures that recommendations are fair and contextually accurate.

Encouraging feedback from applicants and employees also promotes transparency and accountability. When organizations combine AI detection with human review, they create a balanced system that continuously improves and supports equitable hiring practices.

Our Tool

We’ve created a demo application that demonstrates the model’s ability to detect gender biases and present them in a way that is easy to understand and action. You can access it here.

Let’s test this with an example to see how it performs. We used a random job listing for a CTO position for a company based in Mumbai. Here’s what the model found:

As you can see, for this job listing, the model found that the job description was heavily biased towards male candidates. This being a CXO level position that is tech oriented, this result is unsurprising considering the contemporary understanding of who should be hired for such a role.

In order to mitigate this, it is important to have a more detailed understanding of the specific ways in which this job description is biased. The tool breaks down the job description into words, and analyzes the wording used. This is presented in a graphical format:

The tool also breaks down which word groups have the most impact when it comes to biases:

Finally, the tool bifurcates the weighting of each word into how biased it is towards either gender:

Future Steps

Our tool focuses on one crucial part of the hiring process: job listings, where bias often begins. However, building a truly equitable workplace requires a broader approach. Below are several ways AI can continue to help organizations reduce bias and promote inclusion.

Inclusion of other types of biases

While this project centers on gender bias, similar techniques can identify other forms of bias such as race, religion, or socio-economic background. Expanding the model to detect these additional biases can make hiring processes even more inclusive.

Business communication

Bias is not limited to job descriptions. It can also appear in company communications, including press releases, websites, and marketing materials. Applying the same model here can help organizations ensure that all forms of communication remain neutral and inclusive. Promoting fairness and transparency in communication is also central to rebuilding public trust. Learn about the top organizations restoring trust by tackling disinformation and how they’re using technology to promote truth and accountability worldwide.

Internal Communication

Bias can occur within teams as well. By analyzing internal communications, companies can encourage respectful and professional interactions among employees. This helps foster a healthier and more collaborative workplace culture.

Employee Satisfaction

AI can also help organizations understand what different employee groups value most. By analyzing feedback and engagement data, businesses can tailor benefits and policies to improve employee retention and satisfaction.

Other Applications

Education

The same technology behind our Gender Bias Detection tool can be applied in many other fields to promote fairness and inclusivity.

Media and Advertising

In media and marketing, the tool can analyze articles, campaigns, and advertisements to spot and reduce gender stereotypes. This helps brands promote balanced representation and connect with audiences more authentically.

Healthcare

In healthcare, the model can review medical documents, patient communications, and research materials to detect bias that affects treatment or access. This supports equitable care and helps improve health outcomes for all genders.

Legal and Justice Systems

The system can also assist in the legal field by examining court records, legal documents, and legislation for biased language. This can promote fairness and help ensure that justice processes remain objective and inclusive.

Want to work with us too?

Let’s see if we are a good fit