Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK
Please log in

Opening the black box: Explainable AI (XAI)

Maren Eckhoff (QuantumBlack)
16:3517:15 Wednesday, 1 May 2019
Data Science, Machine Learning & AI, Expo Hall
Location: Expo Hall (Capital Hall N24)
Secondary topics:  Ethics, Security and Privacy
Average rating: ****.
(4.50, 4 ratings)

Who is this presentation for?

  • Data scientists, machine learning engineers, and data practitioners



Prerequisite knowledge

  • Familiarity with machine learning

What you'll learn

  • Gain an overview of modern explainability techniques
  • Learn how to apply XAI methods to your model
  • Explore applications and use cases for model explanations


The success of machine learning algorithms in a wide range of industries and domains has led to a desire to leverage their power in ever more areas. To drive adoption and gain a deeper understanding of what the model has learned, explainability is top of mind for the machine learning community. Explanations also help manage ethical, legal, and business risks.

Maren Eckhoff discusses modern XAI approaches that increase the transparency of black box algorithms by providing explanations for each prediction made. Many of these methods can be applied to any model, including tree ensembles and neural networks, without limiting their performance. Join in to explore techniques and open source implementations, illustrated with real-world examples.

Photo of Maren Eckhoff

Maren Eckhoff


Maren Eckhoff is a principal data scientist at QuantumBlack, where she leads the analytics work on client projects, working across industries on predictive, explanatory, and optimization problems. Her role includes defining the analytical approach, developing the code base, building models, and communicating the results. Maren also leads the technical training program for QuantumBlack’s data science team and arranges bespoke trainings, seminars, and conference attendance. Previously, Maren worked in demand forecasting. She holds a PhD in probability theory from the University of Bath.