Girls in Tech x UBC: Hack for Humanity Hackathon Recap
This weekend, RightMesh was proud to sponsor, mentor and judge the first Hack for Humanity hackathon hosted by the Vancouver chapter of Girls in Tech and the University of British Columbia. 13 outstanding hacks were built and presented by the 75 participants, 80% of whom were women.
We were especially proud to partner on this female-focused hackathon after just having announced that as a tech organization in Canada, we have 51% female representation in the office (18 women / 17 men).
10 people from the RightMesh team attended to provide mentorship and support for participants. RightMesh sponsored a prize for the Best App for Social Impact which was judged and awarded by Jenna Mortemore, Product Manager and Brianna MacNeil, Blockchain Product Manager. Projects were judged on the combination of technical execution, the demo/presentation, innovation and social impact.
Winner of Best App for Social Impact
Name: Online Sex-Work Risk Predictor
Created by: Akshi Chaudhary, Simran Sethi
Github: https://github.com/akshi8/Risk-predictor
Inspiration: This app was developed to predict the risk for an individual to be a victim of online sex trade or getting drawn into online sex work. A lot of users on adult website fall prey to the sex-trade and human trafficking and online platforms contribute almost 50% of sex-trade venue (source).
Sex trafficking and child pornography are very big issues and gross violation of humanity. The analysis deployed on the WebApp can be used by law enforcement bodies (Police), social groups, college authorities to gauge the effect of certain individuals based on their online activity on such sites to protect them from the harm of sex-trade and potential drug abuse.
Why did we select them for Best App for Social Impact? We selected this project for the award because it was an excellent use of data science for social good. In the future, these insights could be plotted geographically to identify areas with high concentrations of at risk individuals for sex work recruitment. These insights could then be provided to social workers and police to plan outreach programs to proactively attempt to mitigate the risk of recruitment for at risk individuals.
Honourable Mentions
Name: PicTalk
What They Do: PicTalk is a Chrome extension that can interpret images allowing blind and visually impaired users to experience all elements of web content. Their solution used machine learning and text to speech to interpret image content and audibly describe it to the user over audio.
Name: ASL Interpreter
What They Do: ASL Interpreter is a mobile app which uses text-recognition tech to provide English-to-ASL translations. The app would detect english phrases and then return the translation in ASL via a series of videos.
The RightMesh team was thoroughly impressed by the use cases tackled by the teams at this event and the quality of work produced. We look forward to working with the Girls in Tech organization in the future to continue to support both women in the technology field as well as technology solutions for humanitarian issues.
