Bachelor and Master Theses

Background: Artificial intelligence (AI) is transforming various industrial sectors, including mining and the pulp and paper industry, by enhancing efficiency and promoting sustainability. One of the significant challenges, however, is the lack of transparency associated with AI models, which often leads to doubt from industry professionals. This is where Explainable AI (XAI) comes into play. Its goal is to make AI model behaviour more understandable and interpretable.

While there are plenty of generic techniques for XAI out there, the adaptation of these methods to fit the workflows of industrial experts is still quite limited. This is a vital gap that needs addressing. Platforms like MainlyAI offer a unique approach by providing interactive environments where users can visualise explanations and seamlessly integrate their feedback into their workflows. This creates a more trustworthy and practical solution for bridging the gap between advanced AI technologies and the expertise of industry professionals.

Scope: This thesis focuses on studying how different explanation modalities can be presented in an interactive graph environment through industrial use cases such as predictive maintenance.

Goals:

· Evaluate the impact of explanation modalities (feature importance, counterfactuals, rule-based explanations) on user comprehension

Task Description:

· Conduct a literature review on explainable AI 

· Select 1–2 industrial use cases (e.g., predictive maintenance, process optimisation).

· Implement ML and Integrate multiple explanation modalities (feature importance, counterfactuals, rule-based) for selected use cases.

· Conduct user evaluations with representative participants (e.g., engineers, process experts, or proxies).

XAI & Machine Learning Expertise Good to have

Python Programming Languages with Machine Learning Frameworks

MainlyAI