arXiv subjects

Daniel Fürst

Publications and source records attributed to Daniel Fürst.

7 recordsLinked to original sources

arXiv2026

Lexplorer: Navigating the Complexity of Legal Document Landscapes

As technological and social innovations create novel regulatory challenges, legal systems grow in complexity - increasing the need for interfaces that enable effective interactions with legal document collections. Through interviews with legal scholars (n=15), we find that supporting legal work requires going beyond retrieval-centered legal-information-system paradigms. Hence, we propose Lexplorer, a flexible interface for exploring, navigating, and analyzing legal documents, based on a taxonomy capturing user intents. Distinguishing text and data views for one, few, and many documents, Lexplorer enables context-sensitive interactions with evolving collections of interconnected legal texts, facilitating Adaptive Meaning Construction in law. We evaluate Lexplorer with legal scholars (n=20) in the context of European Union law, validating our elicited requirements, intent taxonomy, and prototype design. Resulting from a close collaboration between visual-analytics researchers and legal scholars, our work also provides nuanced insights into the process required to design interactive systems for expert domains driven by implicit methodological knowledge.

cs.HC↗

arXiv2025

SemanticTours: A Conceptual Framework for Non-Linear, Knowledge Graph-Driven Data Tours

Interactive tours help users explore datasets and provide onboarding. They rely on a linear sequence of views, showing a curated set of relevant data selections and introduce user interfaces. Existing frameworks of tours, however, often do not allow for branching and refining hypotheses outside of a rigid sequence, which is important in knowledge-centric domains such as law. For example, lawyers performing analytical case analysis need to iteratively weigh up different legal norms and construct strings of arguments. To address this gap, we propose SemanticTours, a semantic, graph-based model of tours that shifts from a sequence-based towards a graph-based navigation. Our model constructs a domain-specific knowledge graph that connects data elements based on user-definable semantic relationships. These relationships enable non-linear graph navigation that defines tours. We apply SemanticTours to the domain of law and conceptualize a visual analytics design and interaction concept for analytical reasoning in legal case analysis. Our concept accounts for the inherent complexity of graph-based tours using aggregated graph nodes and supporting navigation with a semantic lens. During an evaluation with six domain experts from law, they suggest that graph-based tours better support their analytical reasoning than sequences. Our work opens research opportunities for such tours to support analytical reasoning in law and other knowledge-centric domains.

cs.HC↗

arXiv2025

Challenges and Opportunities for Visual Analytics in Jurisprudence

Legal exploration, analysis, and interpretation remain complex and demanding tasks, even for experienced legal scholars, due to the domain-specific language, tacit legal concepts, and intentional ambiguities embedded in legal texts. In related, text-based domains, Visual Analytics (VA) has become an indispensable tool for navigating documents, representing knowledge, and supporting analytical reasoning. However, legal scholarship presents distinct challenges: it requires managing formal legal structure, drawing on tacit domain knowledge, and documenting intricate and accurate reasoning processes - needs that current VA system designs for law fail to address adequately. We identify and describe key challenges and underexplored opportunities in applying VA to law, exploring how these technologies might better serve the legal domain. Interviews with nine legal experts reveal that current legal information retrieval interfaces do not adequately support the navigational complexity of law, often forcing users to rely on internalized legal expertise instead. To address this gap, we identify a three-phase workflow for legal experts, which highlights opportunities for VA to support legal reasoning through knowledge externalization and provenance tracking, leveraging tree-, graph-, and hierarchy-based visualizations. Through this contribution, our work establishes a user-centered VA workflow for the legal domain, recognizing tacit legal knowledge as a critical element of sense-making and insight generation, and situates these contributions within a broader research agenda for VA in law and other text-based disciplines.

cs.HC↗

arXiv2025

Understanding Large Language Model Behaviors through Interactive Counterfactual Generation and Analysis

Understanding the behavior of large language models (LLMs) is crucial for ensuring their safe and reliable use. However, existing explainable AI (XAI) methods for LLMs primarily rely on word-level explanations, which are often computationally inefficient and misaligned with human reasoning processes. Moreover, these methods often treat explanation as a one-time output, overlooking its inherently interactive and iterative nature. In this paper, we present LLM Analyzer, an interactive visualization system that addresses these limitations by enabling intuitive and efficient exploration of LLM behaviors through counterfactual analysis. Our system features a novel algorithm that generates fluent and semantically meaningful counterfactuals via targeted removal and replacement operations at user-defined levels of granularity. These counterfactuals are used to compute feature attribution scores, which are then integrated with concrete examples in a table-based visualization, supporting dynamic analysis of model behavior. A user study with LLM practitioners and interviews with experts demonstrate the system's usability and effectiveness, emphasizing the importance of involving humans in the explanation process as active participants rather than passive recipients.

cs.CL↗

arXiv2024

iNNspector: Visual, Interactive Deep Model Debugging

Deep learning model design, development, and debugging is a process driven by best practices, guidelines, trial-and-error, and the personal experiences of model developers. At multiple stages of this process, performance and internal model data can be logged and made available. However, due to the sheer complexity and scale of this data and process, model developers often resort to evaluating their model performance based on abstract metrics like accuracy and loss. We argue that a structured analysis of data along the model's architecture and at multiple abstraction levels can considerably streamline the debugging process. Such a systematic analysis can further connect the developer's design choices to their impacts on the model behavior, facilitating the understanding, diagnosis, and refinement of deep learning models. Hence, in this paper, we (1) contribute a conceptual framework structuring the data space of deep learning experiments. Our framework, grounded in literature analysis and requirements interviews, captures design dimensions and proposes mechanisms to make this data explorable and tractable. To operationalize our framework in a ready-to-use application, we (2) present the iNNspector system. iNNspector enables tracking of deep learning experiments and provides interactive visualizations of the data on all levels of abstraction from multiple models to individual neurons. Finally, we (3) evaluate our approach with three real-world use-cases and a user study with deep learning developers and data analysts, proving its effectiveness and usability.

cs.HC↗

arXiv2024

MelodyVis: Visual Analytics for Melodic Patterns in Sheet Music

Manual melody detection is a tedious task requiring high expertise level, while automatic detection is often not expressive or powerful enough. Thus, we present MelodyVis, a visual application designed in collaboration with musicology experts to explore melodic patterns in digital sheet music. MelodyVis features five connected views, including a Melody Operator Graph and a Voicing Timeline. The system utilizes eight atomic operators, such as transposition and mirroring, to capture melody repetitions and variations. Users can start their analysis by manually selecting patterns in the sheet view, and then identifying other patterns based on the selected samples through an interactive exploration process. We conducted a user study to investigate the effectiveness and usefulness of our approach and its integrated melodic operators, including usability and mental load questions. We compared the analysis executed by 25 participants with and without the operators. The study results indicate that the participants could identify at least twice as many patterns with activated operators. MelodyVis allows analysts to steer the analysis process and interpret results. Our study also confirms the usefulness of MelodyVis in supporting common analytical tasks in melodic analysis, with participants reporting improved pattern identification and interpretation. Thus, MelodyVis addresses the limitations of fully-automated approaches, enabling music analysts to step into the analysis process and uncover and understand intricate melodic patterns and transformations in sheet music.

cs.HC↗

arXiv2020

Augmenting Sheet Music with Rhythmic Fingerprints

In this paper, we bridge the gap between visualization and musicology by focusing on rhythm analysis tasks, which are tedious due to the complex visual encoding of the well-established Common Music Notation (CMN). Instead of replacing the CMN, we augment sheet music with rhythmic fingerprints to mitigate the complexity originating from the simultaneous encoding of musical features. The proposed visual design exploits music theory concepts such as the rhythm tree to facilitate the understanding of rhythmic information. Juxtaposing sheet music and the rhythmic fingerprints maintains the connection to the familiar representation. To investigate the usefulness of the rhythmic fingerprint design for identifying and comparing rhythmic patterns, we conducted a controlled user study with four experts and four novices. The results show that the rhythmic fingerprints enable novice users to recognize rhythmic patterns that only experts can identify using non-augmented sheet music.

cs.HC↗