Sep 9–12, 2019

A practical guide toward explainability and bias evaluation in AI and machine learning

Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
2:35pm3:15pm Thursday, September 12, 2019
Location: LL21 C/D
Average rating: ****.
(4.00, 3 ratings)

Who is this presentation for?

  • Software engineers, data scientists, managers, senior technical leaders, product managers, delivery managers, machine learning engineers, and researchers




The concepts of “undesired bias” and “black box models” in machine learning have become a highly discussed topic due to the numerous high profile incidents that have been covered by the media. It’s certainly a challenging topic, as it could even be said that the concept of societal bias is inherently biased in itself, depending on an individual’s (or group’s) perspective.

Alejandro Saucedo doesn’t reinvent the wheel; he simplifies the issue of AI explainability so it can be solved using traditional methods. He covers the high-level definitions of bias in machine learning to remove ambiguity and demystifies it through a hands-on example, in which the objective is to automate the loan-approval process for a company using machine learning, which allows you to go through this challenge step by step and use key tools and techniques from the latest research together with domain expert knowledge at the right points to enable you to explain decisions and mitigate undesired bias in machine learning models.

Alejandro breaks undesired bias down into two constituent parts: a priori societal bias and a posteriori statistical bias, with tangible examples of how undesired bias is introduced in each step, and you’ll learn some very interesting research findings in this topic. Spoiler alert: Alejandro takes a pragmatic approach, showing how any nontrivial system will always have an inherent bias, so the objective is not to remove bias, but to make sure you can get as close as possible to your objectives and make sure your objectives are as close as possible to the ideal solution.

Prerequisite knowledge

  • Experience with a machine learning project

What you'll learn

  • Gain an overview of the concept of bias in machine learning
  • Learn the three key steps to assess bias throughout the lifecycle of a machine learning model
  • Understand how key machine learning concepts, such as feature importance, class imbalance, model analysis, partial dependence, etc., are used in a practical example, as well as how these data science fundamentals can be used to interact with key domain experts
Photo of Alejandro Saucedo

Alejandro Saucedo

The Institute for Ethical AI & Machine Learning

Alejandro Saucedo is the chief scientist at the Institute for Ethical AI & Machine Learning, where he leads highly technical research on machine learning explainability, bias evaluation, reproducibility and responsible design. Previously, Alejandro held technical leadership positions across hypergrowth scale-ups and tech giants including Eigen Technologies, Bloomberg LP, and Hack Partners. He has a strong track record of building departments of machine learning engineers from scratch and leading the delivery of large-scale machine learning system across the financial, insurance, legal, transport, manufacturing, and construction sectors (in Europe, the US, and Latin America).

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Picture of Alejandro Saucedo
Alejandro Saucedo | Chairman
09/13/2019 2:00am PDT

Thank you very much Brigitte, really glad you enjoyed it. You can find the slides and video for the talk in the repo at

brigitte custer | VP, Chief Technology Officer
09/13/2019 12:41am PDT

Excellent presentation. Please post the briefing slides. Thank you

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