Probing Interactive Explainable AI
Description: Kaur et al. (2020) showed that data scientists are unable to detect common issues in AI systems using existing explainable AI (XA) methods. There are multiple tools that try to simplify working with XAI methods, like explAIner (Spinner et al., 2020), BEAMES (Das et al., 2019), QUESTO (Das et al., 2020), and the What-If-Tool.
The goal of this project is to compare interactive XAI tools and investigate whether they support users better in finding issues in AI systems. For this, the contextual inquiry used by Kaur et al. (2020) can be replicated with a choice of interactive XAI tools.
Supervisor: Susanne Hindennach
Distribution: 20% Literature, 20% Experiment design, 20% Implementation, 40% Data analysis
Requirements: Interest in XAI and (UX) study design
Das, S. D. Cashman, R. Chang and A. Endert. 2019. BEAMES: Interactive Multimodel
Steering, Selection, and Inspection for Regression Tasks. IEEE Computer Graphics
and Applications, 39(5), p.20-32.
Das, S., S. Xu, M. Gleicher, R. Chang, and A. Endert. 2020. QUESTO: Interactive
Construction of Objective Functions for Classification Tasks. Computer Graphics
Forum, 39(3), p.153–165.
Kaur, H., H. Nori, S. Jenkins, R. Caruana, H. Wallach and J. Wortman Vaughan. 2020. Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability
Tools for Machine Learning. Proceedings of the Conference on Human Factors in Computing Systems (CHI), p.1-14.
Spinner, T., U. Schlegel, H. Schäfer, and M. El-Assady. 2020. explAIner: A visual analytics
framework for interactive and explainable machine learning. IEEE Transactions on
Visualization and Computer Graphics, 26(1), p.1064–1074.