Bias

Datacast Episode 67: Model Observability, AI Ethics, and ML Infrastructure Ecosystem with Aparna Dhinakaran

Datacast Episode 67: Model Observability, AI Ethics, and ML Infrastructure Ecosystem with Aparna Dhinakaran

Aparna Dhinakaran is the Chief Product Officer at Arize AI, a startup focused on ML Observability. She was previously an ML engineer at Uber, Apple, and TubeMogul (acquired by Adobe). During her time at Uber, she built several core ML Infrastructure platforms, including Michelangelo. She has a bachelor’s from Berkeley's Electrical Engineering and Computer Science program, where she published research with Berkeley's AI Research group. She is on a leave of absence from the Computer Vision Ph.D. program at Cornell University.

What I Learned From Attending REWORK MLOps and ML Fairness Summits

What I Learned From Attending REWORK MLOps and ML Fairness Summits

Last month, I attended two great summits organized by REWORK: The MLOps summit that discovers how to optimize the ML lifecycle & streamline ML pipeline for better production and the ML Fairness summit that discovers strategies to ensure ML models are accountable & fair to build secure & responsible AI. As a previous attendee of REWORK’s in-person summit, I have always enjoyed the unique mix of academia and industry, enabling attendees to meet with AI pioneers at the forefront of research and explore real-world case studies to discover the business value of AI.

In this long-form blog recap, I will dissect content from the talks that I found most useful from attending the summit. The post consists of 10 talks that range from automated data labeling and pipeline optimization, to model fairness and responsible AI at scale.

What I Learned From Attending Scale Transform 2021

What I Learned From Attending Scale Transform 2021

A few weeks ago, I attended Transform, Scale AI’s first-ever conference that brought together an all-star line-up of the leading AI researchers and practitioners. The conference featured 19 sessions discussing the latest research breakthroughs and real-world impact across industries.

In this long-form blog recap, I will dissect content from the session talks that I found most useful from attending the conference. These talks cover everything from the future of ML frameworks and the importance of a data-centric mindset to AI applications at companies like Facebook and DoorDash. To be honest, the conference's quality was so amazing, and it’s hard to choose the talks to recap.

Datacast Episode 48: AI Ethics, Open Data, and Recommendations Fairness with Jessie Smith

Datacast Episode 48: AI Ethics, Open Data, and Recommendations Fairness with Jessie Smith

Jessie J. Smith (Jess) is a second-year Ph.D. student in the Department of Information Science at the University of Colorado Boulder. Her Ph.D. research focuses on AI ethics, machine learning fairness and bias, and ethical speculation in the computer science classroom. Since receiving her Bachelor's in Software Engineering, Jess works to engage in public scholarship about her research to encourage transparency and interdisciplinary dialogue about technology's unintended consequences. She is also the co-host and co-creator of The Radical AI Podcast.