Lectures  |    |  June 18, 2019

Guide towards algorithm explainability in machine learning

Runtime 39 minutes.

Lecture presented by Alejandro Saucedo at the PyData London 2019 conference. Runtime 39 minutes.

Undesired bias in machine learning has become a worrying topic due to the numerous high profile incidents. In this talk we demystify machine learning bias through a hands-on example. We’ll be tasked to automate the loan approval process for a company, and introduce key tools and techniques from latest research that allow us to assess and mitigate undesired bias in our machine learning models.

Alejandro Saucedo is the chief scientist at the Institute for Ethical AI & Machine Learning. In his more than 10 years of software development experience, Alejandro has held technical leadership positions across hypergrowth scale-ups and tech giants including Eigen Technologies, Bloomberg LP, and Hack Partners. Alejandro has a strong track record of building multiple departments of machine learning engineers from scratch and leading the delivery of numerous large-scale machine learning systems across the financial, insurance, legal, transport, manufacturing, and construction sectors in Europe, the US, and Latin America.

Slides and code at https://github.com/EthicalML/explainability-and-bias/