Unconscious/implicit bias are social stereotypes about certain groups of people that individuals form outside of their own conscious awareness. It plays a big role in the applicant selection process and can create a barrier to entry for minorities with ethnic-sounding names. Minorities who "whiten" their resumes get more callbacks for interviews versus the one's who do not.
Anonly tries to tackle this problem by removing the applicant's personal information and only listing their skills, experience, and education. The interviewer can only see the applicant's name after they send out interview requests thereby eliminating subconscious bias impacting the selection process.
HTML, CSS, JavaScript, Semantic-UI, ReactJS and Google Firebase.
Implementing PDFMiner, a tool for extracting information from PDF documents. Unlike other PDF-related tools, it focuses entirely on getting and analyzing text data. PDFMiner allows one to obtain the exact location of text in a page, as well as other information such as fonts or lines.
This technology would allow Anonly pull specific user data from the uploaded resume (e.g. skills, experience). This will improve user experience and ensure to users and hiring managers that unconscious bias does not seep into the selection process.
Our team won Google's Best Use of Google Cloud Platform challenge.