Rita Borgo

Daniel Braun, Kerstin Ebell, Vera Schemann et al.

This paper presents a novel color scheme designed to address the challenge of visualizing data series with large value ranges, where scale transformation provides limited support. We focus on meteorological data, where the presence of large value ranges is common. We apply our approach to meteorological scatterplots, as one of the most common plots used in this domain area. Our approach leverages the numerical representation of mantissa and exponent of the values to guide the design of novel "nested" color schemes, able to emphasize differences between magnitudes. Our user study evaluates the new designs, the state of the art color scales and representative color schemes used in the analysis of meteorological data: ColorCrafter, Viridis, and Rainbow. We assess accuracy, time and confidence in the context of discrimination (comparison) and interpretation (reading) tasks. Our proposed color scheme significantly outperforms the others in interpretation tasks, while showing comparable performances in discrimination tasks.

Daniel Braun, Rita Borgo, Max Sondag et al.

We introduce two novel visualization designs to support practitioners in performing identification and discrimination tasks on large value ranges (i.e., several orders of magnitude) in time-series data: (1) The order of magnitude horizon graph, which extends the classic horizon graph; and (2) the order of magnitude line chart, which adapts the log-line chart. These new visualization designs visualize large value ranges by explicitly splitting the mantissa m and exponent e of a value v = m * 10e . We evaluate our novel designs against the most relevant state-of-the-art visualizations in an empirical user study. It focuses on four main tasks commonly employed in the analysis of time-series and large value ranges visualization: identification, discrimination, estimation, and trend detection. For each task we analyse error, confidence, and response time. The new order of magnitude horizon graph performs better or equal to all other designs in identification, discrimination, and estimation tasks. Only for trend detection tasks, the more traditional horizon graphs reported better performance. Our results are domain-independent, only requiring time-series data with large value ranges.

Yiwen Xing, Cristina Dondi, Rita Borgo et al.

The circulation of historical books has always been an area of interest for historians. However, the data used to represent the journey of a book across different places and times can be difficult for domain experts to digest due to buried geographical and chronological features within text-based presentations. This situation provides an opportunity for collaboration between visualization researchers and historians. This paper describes a design study where a variant of the Nine-Stage Framework was employed to develop a Visual Analytics (VA) tool called DanteExploreVis. This tool was designed to aid domain experts in exploring, explaining, and presenting book trade data from multiple perspectives. We discuss the design choices made and how each panel in the interface meets the domain requirements. We also present the results of a qualitative evaluation conducted with domain experts. The main contributions of this paper include: 1) the development of a VA tool to support domain experts in exploring, explaining, and presenting book trade data; 2) a comprehensive documentation of the iterative design, development, and evaluation process following the variant Nine-Stage Framework; 3) a summary of the insights gained and lessons learned from this design study in the context of the humanities field; and 4) reflections on how our approach could be applied in a more generalizable way.

Munkhtulga Battogtokh, Yiwen Xing, Cosmin Davidescu et al.

In natural language processing (NLP), text classification tasks are increasingly fine-grained, as datasets are fragmented into a larger number of classes that are more difficult to differentiate from one another. As a consequence, the semantic structures of datasets have become more complex, and model decisions more difficult to explain. Existing tools, suited for coarse-grained classification, falter under these additional challenges. In response to this gap, we worked closely with NLP domain experts in an iterative design-and-evaluation process to characterize and tackle the growing requirements in their workflow of developing fine-grained text classification models. The result of this collaboration is the development of SemLa, a novel visual analytics system tailored for 1) dissecting complex semantic structures in a dataset when it is spatialized in model embedding space, and 2) visualizing fine-grained nuances in the meaning of text samples to faithfully explain model reasoning. This paper details the iterative design study and the resulting innovations featured in SemLa. The final design allows contrastive analysis at different levels by unearthing lexical and conceptual patterns including biases and artifacts in data. Expert feedback on our final design and case studies confirm that SemLa is a useful tool for supporting model validation and debugging as well as data annotation.

Rita Borgo, Darren J Edwards

Defining trust is an important endeavor given its applicability to assessing public mood to much of the innovation in the newly formed autonomous industry, such as artificial intelligence (AI),medical bots, drones, autonomous vehicles, and smart factories [19].Through developing a reliable index or means to measure trust,this may have wide impact from fostering acceptance and adoption of smart systems to informing policy makers about the public atmosphere and willingness to adopt innovate change, and has been identified as an important indicator in a recent UK policy brief [8].In this paper, we reflect on the importance and potential impact of developing Visualization Psychology in the context of solving definitions and policy decision making problems for complex constructs such as "trust".

Alfie Abdul-Rahman, Rita Borgo, Min Chen et al.

Empirical studies form an integral part of visualization research. Not only can they facilitate the evaluation of various designs, techniques, systems, and practices in visualization, but they can also enable the discovery of the causalities explaining why and how visualization works. This state-of-the-art report focuses on controlled and semi-controlled empirical studies conducted in laboratories and crowd-sourcing environments. In particular, the survey provides a taxonomic analysis of over 129 empirical studies in the visualization literature. It juxtaposes these studies with topic developments between 1978 and 2017 in psychology, where controlled empirical studies have played a predominant role in research. To help appreciate this broad context, the paper provides two case studies in detail, where specific visualization-related topics were examined in the discipline of psychology as well as the field of visualization. Following a brief discussion on some latest developments in psychology, it outlines challenges and opportunities in making new discoveries about visualization through empirical studies.

Rita Borgo, Michael Cashmore, Daniele Magazzeni

In order to engender trust in AI, humans must understand what an AI system is trying to achieve, and why. To overcome this problem, the underlying AI process must produce justifications and explanations that are both transparent and comprehensible to the user. AI Planning is well placed to be able to address this challenge. In this paper we present a methodology to provide initial explanations for the decisions made by the planner. Explanations are created by allowing the user to suggest alternative actions in plans and then compare the resulting plans with the one found by the planner. The methodology is implemented in the new XAI-Plan framework.

Min Chen, Luciano Floridi, Rita Borgo

Whenever a visualization researcher is asked about the purpose of visualization, the phrase "gaining insight" by and large pops out instinctively. However, it is not absolutely factual that all uses of visualization are for gaining a deep understanding, unless the term insight is broadened to encompass all types of thought. Even when insight is the focus of a visualization task, it is rather difficult to know what insight is gained, how much, or how accurate. In this paper, we propose that "saving time" in accomplishing a user's task is the most fundamental objective. By giving emphasis to saving time, we can establish a concrete metric, alleviate unnecessary contention caused by different interpretations of insight, and stimulate new research efforts in some aspects of visualization, such as empirical studies, design optimisation and theories of visualization.