Digital Discrimination: Cognitive Bias in Machine Learning

Maureen McElaney

Abstract

With increasing regularity we see stories in the news about machine learning algorithms causing real-world harm. People's lives and livelihood are affected by the decisions made by machines. Learn about how bias can take root in machine learning algorithms and ways to overcome it. From the power of open source, to tools built to detect and remove bias in machine learning models, there is a vibrant ecosystem of contributors who are working to build a digital future that is inclusive and fair. Now you can become part of the solution.

This talk reviews the problem of bias in machine learning and reviews open source solutions to help mitigate bias in machine learning algorithms. Attendees will learn some of the basics of machine learning and model evaluation and training. This talk would be interesting to developers and data scientists who are familiar with machine learning, but there is still plenty for people who are introductory or just interested in bias in tech generally.

Speaker

Photo of Maureen McElaney

Developer Advocate at IBM Center for Open-Source Data and AI Technologies. I am on the Trusted AI Committee underneath LF AI, an umbrella organization underneath the Linux Foundation. I am on the board of the Vermont Tech Alliance and I co-organize Vermont Women in Machine Learning and Data Science.

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