Inspiration
Erin Owen, Grinnell College's class of '94, gave a talk about gender equity regarding venture capitalist funding. Specifically, she discussed how women often get asked questions that are more preventative in nature rather than promotional, which causes women to give more preventative answers and gain less funding overall. This bias is a problem for generating equality in business and is interestingly seen regardless of the interviewer's gender. Therefore, trying to eliminate the presence of this bias using technology could be very helpful.
What it does
Our program takes a question inputted from the user and flags it for any negative (preventional) or positive (promotional) trigger words. After determining the question's overall balance of positivity (determined by the relative presence of positive to negative words), it generates a new question for the user if the negativity of the question is beyond a certain threshold. The new question that is generated closely mirrors the original intent of the question that was asked, but is phrased in a more promotional way, trying to push for always asking promotional questions and creating a positive interview experience for any interviewee.
How I built it
We built this entire program in C. Additionally, we created some text files that our program utilizes when running and making comparisons.
Challenges I ran into
Because our group consists of all first-years, we were quite limited in how we could approach this problem even though we came up with many great ideas on what we could do. Specifically, we were restricted to writing the program in C, which made some of the processing we desired unnecessarily difficult. We also do not have experience with web or app development yet, which made it hard for us to generate a polished product from our base program. This is why our final product is a command line program.
Accomplishments that I'm proud of
We are all very proud of ourselves for participating in a Hackathon for the first time and for actually creating a program that works quite well. We believe our program is a true step in the right direction for helping alleviate gender inequity in this specific area, and we are proud that we were able to generate a functioning program despite all of the challenges we faced. We're also proud that we were able to do this with our limited knowledge and are excited to consider what we could do knowing more in the future.
What I learned
We learned a lot about the intricacies of C, memory allocation, teamwork dynamics, and the issues surrounding gender equity in business.
What's next for pieG: The Positive Interview Experience Generator
Future steps for this program include making it function with speech to text based input by connecting it with a speech recognition program. For the program we already have, we want to improve it by making its bias detection and comparison stronger, adding a feature that lets the user keep on inputting questions until they choose to exit the program (a while loop), and generate a status report for one use of the program that summarizes a given interview and provides information on the overall behavior of the interviewer. We would also like to expand it so that it functions as an independent app and is not tethered to a specific workstation.
Log in or sign up for Devpost to join the conversation.