Rashmi Nagpal

Machine Learning Engineer at Patchstack

Rashmi Nagpal is a Software Engineer with 3+ years of work experience. Also, She is a Research Affiliate at the University of San Francisco, CA, working in sociolinguistics. She is passionate about exploring FATE (Fairness, Accountability, Transparency, and Ethics) in NLP algorithms. She a graduate in Artificial Intelligence from the University of California Berkeley and Plaksha. She is a leader at Women Who Go, wherein she is actively involved in bridging the gender gap in Science, Technology, Engineering, and Mathematics (STEM) fields.


Elixir, Erlang, and the Quest for AI Justice. Building Unbiased ML Systems

Audience: Introductory and overview

In the era of artificial intelligence, fairness is our North Star. Lets discover how Elixir and Erlang rise as champions of AI justice, illuminating the shadows of algorithmic bias and discrimination with astonishing statistics! In this talk, we will venture into the uncharted territory of unbiased machine learning and ponder the timeless wisdom of Martin Luther King Jr.: ‘The time is always right to do what is right.’ and embark on a mission to ensure fairness in AI.

This talk is not a mere summons; it’s a call to action that transcends awareness. As technologist Grace Hopper once noted, “The most dangerous phrase in the language is, ‘We’ve always done it this way.’” Lets understand how Elixir and Erlang wield their power not just as tools but as gatekeepers of fairness and sentinels against bias in AI.

OBJECTIVES: The three key takeaways of my talk include: 1) Increase awareness about the pervasive issue of algorithmic bias and the importance of fairness in AI. 2) Showcase how Elixir and Erlang can be harnessed to build unbiased machine learning systems and promote AI justice. 3) Motivate the audience to take action, emphasizing that it’s the right time to work towards ethical AI and to enforce fairness in the field..

AUDIENCE: Almost open to everyone, since I will begin from basics before jumping to advance concepts but will highly useful to developers, data scientists :)