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Data science & AI: Towards a Quantitative Evaluation Metric for XAI

Posted 30 Dec 2022
Work experience
0 to 1 years
Full-time / part-time
Job function
€1,000 per month
Degree level
Required language
Dutch (Fluent)

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Master's thesis: Towards a Quantitative Evaluation Metric for XAI



Finding a quantitative evaluation metric that can be used to estimate the usefulness of ML model explanations. That wil be the goal of your research in this thesis.

Required interest(s)

  • Explainable AI
  • Artificial Intelligence
  • Machine Learning

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

As machine learning (ML) systems take a more prominent and central role in contributing to life-impacting decisions, ensuring their trustworthiness and accountabilityis of utmostimportance. Explanations sit at the core of these desirable attributes of a ML system. The emerging field is frequently called “Explainable AI (XAI)” or “Explainable ML.” The goal of explainable ML is to intuitively ex-plain the predictions of a ML system, while adhering to the needs to various stakeholders. Many explanation techniques were developed with contributions from both academia and industry. However, there are several existing challenges that have not garnered enough interest and serve as roadblocks to widespread adoption of explainable ML.

It is difficult to determine which eXplainable AI technique (XAI) is most useful in a given scenario. A proper quantitative evaluation metric to determine this does not exist at the moment. As a result, it is unknown whether the explanations created for a certain model are sufficient and usable.

The goal of your research is to find a quantitative evaluation metric that can be used to estimate the usefulness of ML model explanations. This should be a stable metric that could even be utilized in an automated MLOps flow, to give the best possible set of explanations for a given ML model. Your results will assist us in implementing XAI solutions at our clients.

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.

Maatwerksoftware bouwen waar miljoenen mensen dagelijks gebruik van maken. Dat is ons werk. Voor grote gerenommeerde klanten in Nederland en België. Betrouwbaar, schaalbaar en onderhoudbaar. Wij gaan voor software oplossingen van zeer hoge kwaliteit. Binnen de afgesproken tijd en het budget.
Door samen continu te vernieuwen helpen we klanten en de wereld significant vooruit. Onze ambitie en drive maakt ons vastberaden om topkwaliteit te leveren en voorop te lopen. Can you do IT?

Active in 2 countries
500 employees
90% men - 10% women
Average age is 30 years

What employees are saying


IT Consultant


Mijn technisch begeleider tijdens mijn afstuderen bij Info Support is een voorbeeld voor mij. Hij heeft twee jaar meer ervaring en is nu teamlead geworden. Dat zou voor mij een mooi streven zijn. Ik heb het aangegeven tijdens mijn ambitiegesprek en we gaan er naartoe werken. Ik kijk ernaar uit!




Ik wilde tijdens mij afstuderen graag aan iets tastbaars werken. Bij Info Support had ik ruime keuze uit opdrachten. Ik koos ervoor om een smartwatch-applicatie te gaan ontwikkelen die mensen met een visuele beperking op treinstations de weg kan wijzen.