Current algorithms meant to function without bias commonly inherit the biases of their programmers. This results from various issues such as the inclusion of traits like race, gender, nationality, etc. In our project, we look specifically at how the use of these algorithms affects the hiring process. In an attempt to reduce bias, we created our own algorithm that does not take into account characteristics such as race, gender, or sex. While this is a simplified version compared to other algorithms currently in use, our application can be built upon for further improvement.
Our grand challenge is finding an accurate way to evaluate and analyze different mental health disorders based off of results from our comprehensive survey, Reach Through using industry standard mental health questionnaires, such as the DSM5, we created a survey that is the first step for people to determine whether or not they want to
While sleep is crucial to most life on Earth, studies have shown that college students on a great scale are one of the most affected in regard to sleep. With college students, sleep is very important for them to stay focused and awake during their classes throughout the day. But, it can be hard due