Inspiration
In the landscape of automated hiring, algorithms can inadvertently perpetuate racial biases. This platform is dedicated to addressing and rectifying this issue by focusing on redacting sensitive information, particularly names, from resumes, thereby promoting fairness and impartiality in hiring processes.
What it does
This project provides a user-friendly interface for a Redaction API, primarily focusing on eliminating bias in the hiring process by redacting names from resumes. Our platform aims to revolutionize resume screening in Applicant Tracking Systems (ATS) by addressing biases associated with names. Our features include- Bias Elimination in Hiring: Aims to eliminate racial bias in resume screening by redacting names from resumes. User-Friendly Interface: Easy to navigate interface for uploading and processing resumes through the Redaction API. Demonstration and Testing: Provides a 'Try Me' section where users can test the functionality of the redaction API.
How we built it
We built it using Angular, Flask and Python
Challenges we ran into
We faced challenges as to how to parse the document, and how to identify the names in the resume pdfs, given that there are different resume templates out there.
Accomplishments that we're proud of
We are proud to present a product that helps to remove bias that
What we learned
We learnt a lot in different areas. We learnt a lot about biases in the system, causes of biases, the innate reasons why the biases have crept into the hiring systems. On the technical aspects, we learnt new tech stack as we both were new to Angular and Flask.
What's next for Resume_Remove_Bias
Next, we'd like to add features like batch processing that would help the companies redact thousands of resumes together before passing them to the ATS.
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