Fairness

Datacast Episode 131: Data Infrastructure for Consumer Platforms, Algorithmic Governance, and Responsible AI with Krishna Gade

Datacast Episode 131: Data Infrastructure for Consumer Platforms, Algorithmic Governance, and Responsible AI with Krishna Gade

Krishna Gade is the founder and CEO of Fiddler AI, an AI Observability startup that helps AI-forward organizations build trusted AI solutions and connect model outcomes to business KPIs. Fiddler addresses problems in model monitoring, explainability, analytics, and fairness.

An entrepreneur and engineering leader with strong technical experience in creating scalable platforms and delightful products, Krishna previously held senior engineering leadership roles at Facebook, Pinterest, Twitter, and Microsoft. At Facebook, Krishna led the News Feed Ranking Platform that created the infrastructure for ranking content and powered use-cases like Facebook Stories and recommendations like People You May Know, Groups You Should Join, etc. Krishna’s team built Facebook’s explainability features like ‘Why am I seeing this?’ which helped bring much-needed algorithmic transparency and, thereby, accountability to the News Feed for both internal and external users.

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.

Datacast Episode 43: From Economics and Operations Management to Data Science with Francesca Lazzeri

Datacast Episode 43: From Economics and Operations Management to Data Science with Francesca Lazzeri

Francesca Lazzeri, Ph.D., is an experienced scientist and machine learning practitioner with over 12 years of academic and industry experience. She is the author of several publications, including technology journals, conferences, and books. She currently leads an international team of cloud advocates and developers at Microsoft, managing an extensive portfolio of customers in the academic/education sector, and building intelligent automated solutions on the Cloud.

Before joining Microsoft, she was a research fellow at Harvard University in the Technology and Operations Management Unit. She is also an advisory board member of the Global Women in Data Science (WiDS) initiative, a machine learning mentor at the Massachusetts Institute of Technology and Columbia University, and an active member of the AI community.