Data Science for Suicide Prevention
Data Science for Suicide Prevention

Abstract: 

In late 2019, a group of researchers discovered that not only does media coverage impact national suicide rates, but that coverage which isn’t aligned to the guidelines established in 2001 for reporting on suicide risks increasing national suicide rates by up to 13% (https://www.bmj.com/content/368/bmj.m575). In response, a team of nonprofit and for-profit organizations came together (including the researcher who created the original reporting guidelines, which have been endorsed by the WHO and CDC) to develop a ML-based UI for journalists (similar to Grammarly) to increase the adoption rate of these standards. In this session, we (if approved, a suicide prevention researcher will co-present with me) will demonstrate how this ML will save hundreds of thousands of lives as well as the best practices we’re using to maximize its technical efficacy and social impact. Note that this session will include a live demo.

Bio: 

Jennifer Redmon joined Cisco in 2009 and serves as its Chief Data Evangelist. She and her team support Cisco’s journey to up-level the company’s analytical acumen through offers such as education. In 2019, she joined The Erika Legacy Foundation pro bono as its Director of Data Science and Artificial Intelligence to honor the foundation’s namesake, Erika, with whom Jennifer had been close friends prior to Erika’s death. Jennifer is passionate about ethical applications of data science, ML/AIs power to create a more compassionate world, DIY home projects, Peloton, and travel. Jennifer holds an international MBA from Duke University with a concentration in Strategy and Bachelor’s Degrees in Economics and Art History from UC Davis.