Susanne Hindennach

Institute for Visualisation and Interactive Systems, University of Stuttgart

  Pfaffenwaldring 5a, 70569 Stuttgart, Germany University of Stuttgart, SimTech Building Google Scholar

Biography

Susanne Hindennach is a PhD student in the Collaborative Artificial Intelligence group since June 2020. She holds a Bachelor’s degree in Cognitive Science from the University of Osnabrück and a Master’s degree in Clinical and Experimental Neuroscience from the University of Cologne. In her PhD, she analyses mind attribution in explanations about AI systems on the one hand, and, on the other hand, she investigates new XAI methods that provide explanations about the humans involved in building the AI system. As such, the goal of her PhD is to redirect Theory of Mind away from the AI systems and towards the human minds behind AI.

Teaching

2022

Machine Perception and Learning (Tutor)Master

 

Medieninformatik (Teaching Assistant)Bachelor

2021

Medieninformatik (Teaching Assistant)Bachelor

2020

Machine Learning Approaches to Understanding User Behavior (Hauptseminar)Master

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Supervision

2024

Explaining Disagreement in Visual Question Answering Using Eye Tracking on Questions (B.Sc.) Amtal Mateen Hina

 

Analyzing Neural Attention in Chart Question Answering (M.Sc.) Yingpeng Ma§§

2023

Difference in Mind Attribution of ChatGPT in Scientific Articles in Comparison to Media Articles (B.Sc.) Marcel Kächele

 

The Need to Explore Different Perspectives: Visual Question Answering and its Variability in Replies Caused by Mental States (B.Sc.) Mah-Rukh Chaudhry

 

A tool for Digitizing the Meal Ordering Process at the Dreifürstensteinschule in Dornstetten (B.Sc.) Ramazan Sarica

 

Recreating False-Belief Tests as Visual Question Answering Tasks (M.Sc.) Michael Erdemann**

2021

An Interactive Information Visualization Tool for Analyzing Relations Between Academic Papers Through User-defined Tags (B.Sc.) Michael Hersam*

*Co-supervised with Nele Quast
**Co-supervised with Matteo Bortoletto
§§Co-supervised with Yao Wang and Ekta Sood

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Publications

(Hindennach et al., 2024; Hindennach et al., 2024; Zhang et al., 2022)

1. 

   Mindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research

   Susanne Hindennach, Lei Shi, Filip Miletic, Andreas Bulling

   Proc. ACM on Human-Computer Interaction (PACM HCI), 8 (CSCW), pp. 1–42, 2024.

   Abstract Links BibTeX Project Best Paper Honourable Mention Award

   When users perceive AI systems as mindful, independent agents, they hold them responsible instead of the AI experts who created and designed these systems. So far, it has not been studied whether explanations support this shift in responsibility through the use of mind-attributing verbs like "to think". To better understand the prevalence of mind-attributing explanations we analyse AI explanations in 3,533 explainable AI (XAI) research articles from the Semantic Scholar Open Research Corpus (S2ORC). Using methods from semantic shift detection, we identify three dominant types of mind attribution: (1) metaphorical (e.g. "to learn" or "to predict"), (2) awareness (e.g. "to consider"), and (3) agency (e.g. "to make decisions"). We then analyse the impact of mind-attributing explanations on awareness and responsibility in a vignette-based experiment with 199 participants. We find that participants who were given a mind-attributing explanation were more likely to rate the AI system as aware of the harm it caused. Moreover, the mind-attributing explanation had a responsibility-concealing effect: Considering the AI experts’ involvement lead to reduced ratings of AI responsibility for participants who were given a non-mind-attributing or no explanation. In contrast, participants who read the mind-attributing explanation still held the AI system responsible despite considering the AI experts’ involvement. Taken together, our work underlines the need to carefully phrase explanations about AI systems in scientific writing to reduce mind attribution and clearly communicate human responsibility.

   doi: 10.1145/3641009

   Paper: hindennach24_pacm.pdf

   Paper Access: https://medium.com/acm-cscw/be-mindful-when-using-mindful-descriptions-in-explanations-about-ai-bfc7666885c6

   @article{hindennach24_pacm, title = {Mindful Explanations: Prevalence and Impact of Mind Attribution in XAI Research}, author = {Hindennach, Susanne and Shi, Lei and Miletic, Filip and Bulling, Andreas}, year = {2024}, pages = {1--42}, volume = {8}, number = {CSCW}, doi = {10.1145/3641009}, journal = {Proc. ACM on Human-Computer Interaction (PACM HCI)}, url = {https://medium.com/acm-cscw/be-mindful-when-using-mindful-descriptions-in-explanations-about-ai-bfc7666885c6} }

1. 

   Explaining Disagreement in Visual Question Answering Using Eye Tracking

   Susanne Hindennach, Lei Shi, Andreas Bulling

   Proc. International Workshop on Pervasive Eye Tracking and Mobile Gaze-Based Interaction (PETMEI), pp. 1–7, 2024.

   Abstract Links BibTeX Project

   When presented with the same question about an image, human annotators often give valid but disagreeing answers indicating that their reasoning was different. Such differences are lost in a single ground truth label used to train and evaluate visual question answering (VQA) methods. In this work, we explore whether visual attention maps, created using stationary eye tracking, provide insight into the reasoning underlying disagreement in VQA. We first manually inspect attention maps in the recent VQA-MHUG dataset and find cases in which attention differs consistently for disagreeing answers. We further evaluate the suitability of four different similarity metrics to detect attention differences matching the disagreement. We show that attention maps plausibly surface differences in reasoning underlying one type of disagreement, and that the metrics complementarily detect them. Taken together, our results represent an important first step to leverage eye-tracking to explain disagreement in VQA.

   doi: 10.1145/3649902.3656356

   Paper: hindennach24_petmei.pdf

   @inproceedings{hindennach24_petmei, title = {Explaining Disagreement in Visual Question Answering Using Eye Tracking}, author = {Hindennach, Susanne and Shi, Lei and Bulling, Andreas}, year = {2024}, pages = {1--7}, doi = {10.1145/3649902.3656356}, booktitle = {Proc. International Workshop on Pervasive Eye Tracking and Mobile Gaze-Based Interaction (PETMEI)} }

1. 

   Predicting Next Actions and Latent Intents during Text Formatting

   Guanhua Zhang, Susanne Hindennach, Jan Leusmann, Felix Bühler, Benedict Steuerlein, Sven Mayer, Mihai Bâce, Andreas Bulling

   Proc. the CHI Workshop Computational Approaches for Understanding, Generating, and Adapting User Interfaces, pp. 1–6, 2022.

   Abstract Links BibTeX Project

   In this work we investigate the challenging task of predicting user intents from mouse and keyboard input as well as gaze behaviour. In contrast to prior work we study intent prediction at two different resolutions on the behavioural timeline: predicting future input actions as well as latent intents to achieve a high-level interaction goal. Results from a user study (N=15) on a sample text formatting task show that the sequence of prior actions is more informative for intent prediction than gaze. Only using the action sequence, we can predict the next action and the high-level intent with an accuracy of 66% and 96%, respectively. In contrast, accuracy when using features extracted from gaze behaviour was significantly lower, at 41% and 46%. This finding is important for the development of future anticipatory user interfaces that aim to proactively adapt to user intents and interaction goals.

   Paper: zhang22_caugaui.pdf

   @inproceedings{zhang22_caugaui, author = {Zhang, Guanhua and Hindennach, Susanne and Leusmann, Jan and Bühler, Felix and Steuerlein, Benedict and Mayer, Sven and Bâce, Mihai and Bulling, Andreas}, title = {Predicting Next Actions and Latent Intents during Text Formatting}, booktitle = {Proc. the CHI Workshop Computational Approaches for Understanding, Generating, and Adapting User Interfaces}, year = {2022}, pages = {1--6} }