Argumentation frameworks (AFs) provide formal approaches for legal reasoning, but identifying sources of ambiguity and explaining argument acceptance remains challenging for non-experts. We present AF-XRAY, an open-source toolkit for exploring, analyzing, and visualizing abstract AFs in legal reasoning. AF-XRAY introduces: (i) layered visualizations based on game-theoretic argument length revealing well-founded derivation structures; (ii) classification of attack edges by semantic roles (primary, secondary, blunders); (iii) overlay visualizations of alternative 2-valued solutions on ambiguous 3-valued grounded semantics; and (iv) identification of critical attack sets whose suspension resolves undecided arguments. Through systematic generation of critical attack sets, AF-XRAY transforms ambiguous scenarios into grounded solutions, enabling users to pinpoint specific causes of ambiguity and explore alternative resolutions. We use real-world legal cases (e.g., Wild Animals as modeled by Bench-Capon) to show that our tool supports teleological legal reasoning by revealing how different assumptions lead to different justified conclusions.

This paper investigates new data exploration experiences that enable blind users to interact with statistical data visualizations—bar plots, heat maps, box plots, and scatter plots—leveraging multimodal data representations. In addition to sonification and textual descriptions that are commonly employed by existing accessible visualizations, our MAIDR (multimodal access and interactive data representation) system incorporates two additional modalities (braille and review) that offer complementary benefits. It also provides blind users with the autonomy and control to interactively access and understand data visualizations. In a user study involving 11 blind participants, we found the MAIDR system facilitated the accurate interpretation of statistical visualizations. Participants exhibited a range of strategies in combining multiple modalities, influenced by their past interactions and experiences with data visualizations. This work accentuates the overlooked potential of combining refreshable tactile representation with other modalities and elevates the discussion on the importance of user autonomy when designing accessible data visualizations.

We propose a new layered visualization in PyArg for grounded labelings of abstract argumentation frameworks. Argument nodes are colored according to their label (IN, OUT, or UNDEC) and have a new length annotation, which is derived from provenance subgraphs. New edge annotations explain an attack-edge’s role in determining the value (label) of nodes in an argumentation framework.

Causal reasoning is crucial for people to understand data, make decisions, or take action. However, individuals often have blind spots and overlook alternative hypotheses, and using only data is insufficient for causal reasoning. We designed and implemented CrowdIDEA, a novel tool consisting of a three-panel integration incorporating the crowd’s beliefs (Crowd Panel with two designs), data analytics (Data Panel), and user’s causal diagram (Diagram Panel) to stimulate causal reasoning. Through an experiment with 54 participants, we showed the significant effects of the Crowd Panel designs on the outcomes of causal reasoning, such as an increased number of causal beliefs generated. Participants also devised new strategies for bootstrapping, strengthening, deepening, and explaining their causal beliefs, as well as taking advantage of the unique characteristics of both qualitative and quantitative data sources to reduce potential biases in reasoning. Our work makes theoretical and design implications for exploratory causal reasoning.