Improve the usability of machine learning model explainers

Research methods to visualize machine learning model interpretations to improve the usability for citizen developers and citizen data scientists.

Required interest(s)

  • Human-Centered and Community-Minded Information Systems
  • Responsible, Explainable, and socially aware data-science

What do you get

  • A challenging assignment within a practical environment
  • € 1000 compensation, € 500 + lease car or € 600 + living space
  • Professional guidance
  • Courses aimed at your graduation period
  • Support from our academic Research center at your disposal
  • Two vacation days per month

What you will do

  • 65% Research
  • 10% Analyze, design, realize
  • 25% Documentation

Machine learning models and deep learning models provide a powerful way to help recommend best actions, predict various outputs and classify data.

But there’s a lot left to be desired when you look at how we want to explain why a machine learning model makes a prediction. Currently there are tools like LIME (Research by Tulio Ribeiro et al.) that make it possible to get an explanation for a prediction. The explanation given by tools like LIME is limited to a list of probabilities that tell us which features contributed to a particular outcome. Presenting this information to someone without the right statistical knowledge is not very user friendly or safe.

About Info Support Research Center

We anticipate on upcoming and future challenges and ensures our engineers develop cutting-edge solutions based on the latest scientific insights. Our research community proactively tackles emerging technologies. We do this in cooperation with renowned scientists, making sure that research teams are positioned and embedded throughout our organisation and our community, so that their insights are directly applied to our business. We truly believe in sharing knowledge, so we want to do this without any restrictions.

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Application procedure

  1. 1
  2. Introductory meeting

    Discuss (study) career, interests and ambitions and introduction Info Support.

  1. 2
  2. Review

    Assessment of professional knowledge and personality (capacity, competences and motives).

  1. 3
  2. Selection interview

    Deepen professional knowledge and personality.

  1. 4
  2. The signing of a contract

    Contract offer and invitation for drawing moments.