Inspiration
Research indicates that human recruiters' gender stereotypes can lead to women having a 69% lower chance of being interviewed. As women in tech, we understand how frustrating and unfair bias in resume scanning can be, which is why we developed a way to eliminate it.
What it does
We developed a tool that neutralizes gender indicators in resumes in both French and English.
How we built it
We used Gemini to build the model and Python, along with the Gradio framework, to create the interface.
Challenges we ran into
One of the main challenges was creating a solution for such a big problem in a short time
Accomplishments that we're proud of
We're happy to have built a tool that takes a small step toward tackling gender inequality in hiring.
What we learned
We gained a deeper understanding of the systemic challenges women face in the workplace, particularly how bias in resume screening can impact opportunities. Additionally, we enhanced our technical skills by learning how to use the Gradio framework to build intuitive user interfaces for machine learning models and how to use Gemini API.
What's next for Unbias
When it comes to hiring, we are hoping that using a tool like Unbias will become the industry standard.
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