Bachelor and Master Theses

Background: Industrial artificial intelligence (AI) applications work with a variety of data types, including sensor signals, images, and textual reports. To make these data-driven models more understandable, it’s essential to not only utilise effective Explainable AI (XAI) techniques but also implement visualisation strategies that minimise cognitive load and enhance users' ability to form mental models. The Company MainlyAI stands out with its graph-based interactive workflows, providing a unique way to present complex explanations in a visual and intuitive manner. This thesis explores how visualisation, combined with the unique characteristics of different data types and a system-level approach to demonstrating XAI techniques, can significantly enhance user understanding in industrial environments.

Scope: The thesis focuses on visualisation strategies, data type considerations, and the demonstration of XAI within the MainlyAI platform. 

Goals:

· Optimise data visualisation 

· Assess the influence of different data types (sensor, image, text) on explanation efficacy and demonstrate the utility XAI concepts.

Task Description:

· Conduct a literature review 

· Select industrial datasets covering different data types (sensor readings, process images, textual logs).

· Implement ML models for these datasets inside MainlyAI platform.

· Design visualisation patterns and interactive controls (e.g., layered graphs, what-if panels, annotated nodes).

· Evaluate explanation effectiveness across different data types with representative users.

· Demonstrate how MainlyAI can illustrate the theoretical underpinnings of XAI methods (e.g., SHAP).

Machine Learning & XAI Expertise Good to have

Python Programming Languages

Machine Learning Libs e.g. Scikit-learn for training AI models.

MainlyAI