262 citations · 358 across the 27 of their papers we have counts for
33 papers
GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations
Kenza Amara, Mennatallah El-Assady, Rex Ying
Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predi…
A Heuristic Approach for Dual Expert/End-User Evaluation of Guidance in Visual Analytics
Davide Ceneda, Christopher Collins, Mennatallah El-Assady +3
Guidance can support users during the exploration and analysis of complex data. Previous research focused on characterizing the theoretical aspects of guidance in visual analytics…
RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback
Yannick Metz, David Lindner, Raphaël Baur +2
To use reinforcement learning from human feedback (RLHF) in practical applications, it is crucial to learn reward models from diverse sources of human feedback and to consider huma…
Visual Explanations with Attributions and Counterfactuals on Time Series Classification
Udo Schlegel, Daniela Oelke, Daniel A. Keim +1
With the rising necessity of explainable artificial intelligence (XAI), we see an increase in task-dependent XAI methods on varying abstraction levels. XAI techniques on a global l…
Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals
Robin Chan, Afra Amini, Mennatallah El-Assady
We present a human-in-the-loop dashboard tailored to diagnosing potential spurious features that NLI models rely on for predictions. The dashboard enables users to generate diverse…
Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings
Lukas Klein, João B. S. Carvalho, Mennatallah El-Assady +3
Explainable AI aims to render model behavior understandable by humans, which can be seen as an intermediate step in extracting causal relations from correlative patterns. Due to th…
Automatic Generation of Socratic Subquestions for Teaching Math Word Problems
Kumar Shridhar, Jakub Macina, Mennatallah El-Assady +3
Socratic questioning is an educational method that allows students to discover answers to complex problems by asking them a series of thoughtful questions. Generation of didactical…
How to Enable Uncertainty Estimation in Proximal Policy Optimization
Eugene Bykovets, Yannick Metz, Mennatallah El-Assady +2
While deep reinforcement learning (RL) agents have showcased strong results across many domains, a major concern is their inherent opaqueness and the safety of such systems in real…
Characterizing Uncertainty in the Visual Text Analysis Pipeline
Pantea Haghighatkhah, Mennatallah El-Assady, Jean-Daniel Fekete +4
Current visual text analysis approaches rely on sophisticated processing pipelines. Each step of such a pipeline potentially amplifies any uncertainties from the previous step. To…
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning
Eugene Bykovets, Yannick Metz, Mennatallah El-Assady +2
Robustness to adversarial perturbations has been explored in many areas of computer vision. This robustness is particularly relevant in vision-based reinforcement learning, as the…
Visual Comparison of Language Model Adaptation
Rita Sevastjanova, Eren Cakmak, Shauli Ravfogel +2
Neural language models are widely used; however, their model parameters often need to be adapted to the specific domains and tasks of an application, which is time- and resource-co…
Lotse: A Practical Framework for Guidance in Visual Analytics
Fabian Sperrle, Davide Ceneda, Mennatallah El-Assady
Co-adaptive guidance aims to enable efficient human-machine collaboration in visual analytics, as proposed by multiple theoretical frameworks. This paper bridges the gap between su…
Beware the Rationalization Trap! When Language Model Explainability Diverges from our Mental Models of Language
Rita Sevastjanova, Mennatallah El-Assady
Language models learn and represent language differently than humans; they learn the form and not the meaning. Thus, to assess the success of language model explainability, we need…
From Correlation to Causation: Formalizing Interpretable Machine Learning as a Statistical Process
Lukas Klein, Mennatallah El-Assady, Paul F. Jäger
Explainable AI (XAI) is a necessity in safety-critical systems such as in clinical diagnostics due to a high risk for fatal decisions. Currently, however, XAI resembles a loose col…
Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning
David Lindner, Mennatallah El-Assady
Reinforcement learning (RL) commonly assumes access to well-specified reward functions, which many practical applications do not provide. Instead, recently, more work has explored…
Characterizing Grounded Theory Approaches in Visualization
Alexandra Diehl, Alfie Abdul-Rahman, Benjamin Bach +5
Grounded theory (GT) is a research methodology that entails a systematic workflow for theory generation grounded on emergent data. In this paper, we juxtapose GT workflows with typ…
CorpusVis: Visual Analysis of Digital Sheet Music Collections
Matthias Miller, Julius Rauscher, Daniel A. Keim +1
Manually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information.…
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
Maximilian T. Fischer, Daniel Seebacher, Rita Sevastjanova +2
Communication consists of both meta-information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network an…
Communication Analysis through Visual Analytics: Current Practices, Challenges, and New Frontiers
Maximilian T. Fischer, Frederik L. Dennig, Daniel Seebacher +2
The automated analysis of digital human communication data often focuses on specific aspects such as content or network structure in isolation. This can provide limited perspective…
Visual Analytics of Conversational Dynamics
Daniel Seebacher, Maximilian T. Fischer, Rita Sevastjanova +2
Large-scale interaction networks of human communication are often modeled as complex graph structures, obscuring temporal patterns within individual conversations. To facilitate th…
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks
Udo Schlegel, Daniela Oelke, Daniel A. Keim +1
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified ou…
Studying Visualization Guidelines According to Grounded Theory
Alexandra Diehl, Matthias Kraus, Alfie Abdul-Rahman +5
Visualization guidelines, if defined properly, are invaluable to both practical applications and the theoretical foundation of visualization. In this paper, we present a collection…
A Comparative Analysis of Industry Human-AI Interaction Guidelines
Austin P. Wright, Zijie J. Wang, Haekyu Park +6
With the recent release of AI interaction guidelines from Apple, Google, and Microsoft, there is clearly interest in understanding the best practices in human-AI interaction. Howev…
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations
Fabian Sperrle, Mennatallah El-Assady, Grace Guo +3
This paper systematically derives design dimensions for the structured evaluation of explainable artificial intelligence (XAI) approaches. These dimensions enable a descriptive cha…