Session: Targeting Bias in AI
I will be presenting on the topic of bias in the field of Artificial Intelligence and Machine Learning. As AI and ML are rising in popularity it is essential that we as a society understand their limitations. My presentation will be going over what algorithmic bias is along with how human bias influences it. I will also be using interactive examples to demonstrate the relevance of this topic to modern society. I will further be going over the origins of bias in AI and what we can do to limit bias. I’ll explain the best practices and what people should keep in mind when attempting to minimize and analyze bias in algorithms and models. Along with that I will share ways to learn more since I believe that learning doesn’t just end after one talk.
This recording is not available yet.
Bio: Megan Jacob
Megan is a tech enthusiast from the Bay Area who is passionate about Artificial Intelligence and Machine Learning. She enjoys using AI for social good and for investigating topics like bias. She has worked on projects involving Machine Learning in the healthcare industry, building models for heart disease prediction and covid-19 analysis. She is also a member of the NYAS Junior Academy where she is working with her team to develop a solution to reduce bias with AI. She also competes in robotics as the lead engineer for her team and is a 2 time state championship qualifier. She is an NCWIT Aspirations in Computing awardee and a recipient of the SWE Stem in Action Award.