The Many Facets Of Fairness

With algorithmic fairness becoming a growing consideration in machine learning development, why not create a singular framework for tackling these concerns? Today, Tulsee, product lead for ML Fairness at Google will provide a quick introduction to the importance and complexities of fairness in ML, and the many different ways we can approach these concerns.

Tulsee Doshi , Product Lead for ML Fairness at Google

With algorithmic fairness becoming a growing consideration in machine learning development, why not create a singular framework for tackling these concerns? Today, Tulsee, product lead for ML Fairness at Google will provide a quick introduction to the importance and complexities of fairness in ML, and the many different ways we can approach these concerns.

Tulsee is the Product Lead for Google’s ML Fairness Effort. In this role, she leads the development of Google-wide resources and best practices for developing more inclusive and diverse products. Prior to ML Fairness, Tulsee worked on the YouTube recommendations team. She received her BS in Symbolic Systems and MS in Computer Science from Stanford University.

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