Smart hire is a tool for minimizing the impact of implicit bias on hiring decisions. It allows employers to review and rate applications without showing them any identifying information about the applicants. After they have rated applications they can choose whether or not to reveal contact information.
This project was inspired by an episode of the podcast Freakonomics. In particular, it was inspired by the discussion with Iris Bohnet, a behavioral economist at Harvard, in which she lays out a basic methodology for making the hiring process more fair and better at predicting job performance.
While the level of interest in mitigating implicit bias varies, all employers have this problem to some extent or another. Social science research suggests that everyone holds unconscious biases regarding race, gender identity, sexual orientation, etc, and that these biases affect our behavior. In order to create a hiring process that is truly equitable and that is based solely on merit, employers must actively seek to mitigate the impact of implicit bias.
This project makes it easy for employers to review job applications without revealing the applicants' names and emails, information that may give clues about someone's race or gender. By hiding this information during the review process, employers using Smart Hire can simply rate the different parts of an application and then receive an aggregate overall score for each application.
- React
- Redux
- Material UI
- Node.js
- Express
- Knex
- PostgreSQL
- Mocha
- Supertest