Fair Hierarchical Clustering

Building a fair machine learning model has become a critical consideration in today’s world. In this episode, we speak with Anshuman Chabra, a Ph.D. candidate in Computer Networks. Chhabra joins us to discuss his research on building fair machine learning models and why it is important. Find out how he modeled the problem and the result found. <a href= "https://dataskeptic.com/blog/episodes/2022/fair-hierarchical-clustering" target="_blank" rel="noopener">Click here to access additional show notes on our webiste!</a> Thanks to our sponsor! <a href="https://astrato.io/dataskeptic" target="_blank" rel= "noopener">https://astrato.io</a> <a href="https://astrato.io/dataskeptic" target="_blank" rel= "noopener">Astrato</a> is a modern BI and analytics platform built for the Snowflake Data Cloud. A next-generation live query data visualization and analytics solution, empowering everyone to make live data decisions.

Guest

Anshuman Chabra: I am an Assistant Professor of Computer Science and Engineering at the University of South Florida where I lead the Pioneering Advancements in Learning Methods (PALM) Lab 🌴. Prior to joining USF in Fall 2024, I received my PhD in Computer Science from UC Davis. My research focuses on: (1) methods for auditing/augmenting the trustworthiness and safety properties (e.g. utility, robustness, and security) of AI/ML models (e.g. LLMs); (2) developing scalable data-centric and parameter-centric learning approaches that improve model interpretability, performance, and safety; and (3) utilizing these methods for improving AI adoption and usage in real-world interdisciplinary domains.