The problems we aim to solve is the presence of biased and stereotype-reinforcing fields in traditional job applications, which can result in unfair judgments. These fields, such as gender or native background, have the potential to trigger biases and perpetuate stereotypes, creating an inequitable hiring process. By eliminating these unnecessary fields in our platform, we strive to foster a more objective evaluation of candidates based on their skills and qualifications, promoting fairness and equal opportunities for all individuals.
The current hiring process is biased against certain groups of people, such as womxn, people of color, from certain countries, past a certain age or below a certain age, not from a certain pedigree and more. The pay is calculated on the basis of these unnecessary factors and not based on the skills of the applicant. People above a certain age limit or belonging to certain countries , pregnant women and even women in general are paid less and not given much opportunity.
A study by the National Bureau of Economic Research found that women earn 17% less than men for the same work, even after controlling for factors such as experience and education.
In line with our approach, we collect relevant applicant details and, to ensure fairness and combat biases, we securely store and verify their previous pay slips. Instead of disclosing specific salary figures, we provide a non-fungible token (NFT) that indicates only the range of their previous pay. This empowers employers to evaluate candidates solely on their abilities, without being influenced by personal information that could trigger bias or discrimination.
We faced challenges in deploying in digitalocean. We were unable to proceed with the implementation due to the unavailability of credit card. But the mentors were helpful and were able to solve this.
Further we faced some difficulty in integrating the backend with the frontend.