Algorithmic bias in machine learning

by Jill Cates

Community, Social, Ethics, and Education Machine Learning & Data Science

Machine learning algorithms are susceptible to both intentional and unintentional bias. Relying on biased algorithms to drive decisions can lead to unfair outcomes that have serious consequences affecting underrepresented groups of people. In this talk, we'll walk through examples of algorithmic bias in machine learning algorithms, explore tools (in Python) that can measure this bias, and discuss good ethics and software engineering strategies to mitigate bias in machine learning algorithms.

About the Author

Jill is a data scientist at BioSymetrics, where she applies machine learning techniques to messy and complex biomedical datasets. She is a member of PyLadies and lives in Toronto, Canada.

Talk Details

Date: Saturday Nov. 16

Location: Round Room (PyData Track)

Begin time: 16:00

Duration: 10 minutes