Rashmi Nagpal

Rashmi is a skilled Software Engineer at Cactus and a researcher at the University of San Francisco, working in Machine Learning. With nearly four years of experience in the IT industry, she has successfully spearheaded product ideation and led redesigns and feature development, reaching millions of users. Rashmi indulges in astronomy, capturing images of the cosmos with her telescope or unwinding with friends over board games when she is not occupied with coding. Additionally, Rashmi is a proud pet parent to her Maltese breed dog, Fluffy.


The Aesthetics of Unbiased Machine Learning Systems: Crafting Fairness in Practice
Rashmi Nagpal

In Artificial Intelligence systems, a renaissance has unfolded in recent years, captivating the imagination of many! However, a startling statistic emerges amidst the enthusiasm: merely 13% of machine learning models are deployed into production!

Indeed, the artistry of building and deploying these systems supersedes mere scientific methodology as these models dwell within the realm of complexity and are characterized by an inherent reliance on data distribution. Have you ever wondered if these models are trained upon biased data they are destined to produce haywire decisions?

In this talk, let us delve into the realm of ethicality, fairness, and unbiasedness, culminating in the seamless integration of machine learning models that embrace ethical considerations in their journey towards production using Python!