NIPS 2018 Expo Panel

Nov. 28, 2022

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How Algorithmic Fairness influences the Product Development Lifecycle

Sponsor: Google AI

Alex Beutel (Google AI)


Addressing fairness in machine learning is an active area of research; from fostering an inclusive workforce that embodies critical and diverse knowledge, to assessing training datasets for potential sources of bias, developing unbiased models, evaluating machine learning models for disparities in performance, and continued testing of final systems for unfair outcomes.

Far from a solved problem, fairness in machine learning presents both an opportunity and a challenge. Google is committed to making progress in all of these areas. We will share a few examples of projects we're working on to address these challenges.