On the Impact of Explanations on Understanding of Algorithmic Decision-Making

Ethical principles for algorithms are gaining importance as more and more stakeholders are affected by "high-risk" algorithmic decision-making (ADM) systems. Understanding how these systems work enables stakeholders to make informed decisions and to assess the systems' adherence to ethical values. Explanations are a promising way to create understanding, but current explainable artificial intelligence (XAI) research does not always consider theories on how understanding is formed and evaluated. In this work, we aim to contribute to a better understanding of understanding by conducting a qualitative task-based study with 30 participants, including "users" and "affected stakeholders". We use three explanation modalities (textual, dialogue, and interactive) to explain a "high-risk" ADM system to participants and analyse their responses both inductively and deductively, using the "six facets of understanding" framework by Wiggins & McTighe. Our findings indicate that the "six facets" are a fruitful approach to analysing participants' understanding, highlighting processes such as "empathising" and "self-reflecting" as important parts of understanding. We further introduce the "dialogue" modality as a valid alternative to increase participant engagement in ADM explanations. Our analysis further suggests that individuality in understanding affects participants' perceptions of algorithmic fairness, confirming the link between understanding and ADM assessment that previous studies have outlined. We posit that drawing from theories on learning and understanding like the "six facets" and leveraging explanation modalities can guide XAI research to better suit explanations to learning processes of individuals and consequently enable their assessment of ethical values of ADM systems.Comment: 17 pages, 2 figures, 1 table, supplementary material included as PDF, submitted to FAccT 2

Passionate Charts: Arguments for Empathetic Emotions in Data Vis

Aristotle has considered the art of communication as a balance of logos, ethos, and pathos. While in science, logos (reason) and, recently also, ethos (morality) are discussed as aspects not to be neglected, pathos (feeling) is seen critically. In this work, we take a historical perspective on pathos and weigh the pros and cons of applying this rhetorical concept to the field of data visualizations. To better understand data, connecting it to the human way of thinking is imperative - appealing to emotions is one building block. The theoretical and empirical basis originates from different scientific fields, like social sciences, economics, and humanities. Tangible techniques to target empathetic emotions in data visualizations are introduced, as well as other rhetorical devices, such as interactivity and contextual framing, are highlighted. Researching these different approaches can provide new insights regarding the creation and influence of empathetic emotions in data visualizations.Comment: 11 pages, 1 figur

"Being Simple on Complex Issues" -- Accounts on Visual Data Communication about Climate Change

Data visualizations play a critical role in both communicating scientific evidence about climate change and in stimulating engagement and action. To investigate how visualizations can be better utilized to communicate the complexities of climate change to different audiences, we conducted interviews with 17 experts in the fields of climate change, data visualization, and science communication, as well as with 12 laypersons. Besides questions about climate change communication and various aspects of data visualizations, we also asked participants to share what they think is the main takeaway message for two exemplary climate change data visualizations. Through a thematic analysis, we observe differences regarding the included contents, the length and abstraction of messages, and the sensemaking process between and among the participant groups. On average, experts formulated shorter and more abstract messages, often referring to higher-level conclusions rather than specific details. We use our findings to reflect on design decisions for creating more effective visualizations, particularly in news media sources geared toward lay audiences. We hereby discuss the adaption of contents according to the needs of the audience, the trade-off between simplification and accuracy, as well as techniques to make a visualization attractive.Comment: 12 pages, 3 figures, 5 table

Subjective visualization experiences: impact of visual design and experimental design

In contrast to objectively measurable aspects (such as accuracy, reading speed, or memorability), the subjective experience of visualizations has only recently gained importance, and we have less experience how to measure it. We explore how subjective experience is affected by chart design using multiple experimental methods. We measure the effects of changes in color, orientation, and source annotation on the perceived readability and trustworthiness of simple bar charts. Three different experimental designs (single image rating, forced choice comparison, and semi-structured interviews) provide similar but different results. We find that these subjective experiences are different from what prior work on objective dimensions would predict. Seemingly inconsequential choices, like orientation, have large effects for some methods, indicating that study design alters decision-making strategies. Next to insights into the effect of chart design, we provide methodological insights, such as a suggested need to carefully isolate individual elements in charts to study subjective experiences.Comment: 19 pages, 5 figures, 2 table

Data journeys in popular science: Producing climate change and COVID-19 data visualizations at Scientific American

Vast amounts of (open) data are increasingly used to make arguments about crisis topics such as climate change and global pandemics. Data visualizations are central to bringing these viewpoints to broader publics. However, visualizations often conceal the many contexts involved in their production, ranging from decisions made in research labs about collecting and sharing data to choices made in editorial rooms about which data stories to tell. In this paper, we examine how data visualizations about climate change and COVID-19 are produced in popular science magazines, using Scientific American, an established English-language popular science magazine, as a case study. To do this, we apply the analytical concept of "data journeys" (Leonelli, 2020) in a mixed methods study that centers on interviews with Scientific American staff and is supplemented by a visualization analysis of selected charts. In particular, we discuss the affordances of working with open data, the role of collaborative data practices, and how the magazine works to counter misinformation and increase transparency. This work provides a theoretical contribution by testing and expanding the concept of data journeys as an analytical framework, as well as practical contributions by providing insight into the data (visualization) practices of science communicators.Comment: 44 pages, 4 figures, 3 boxe

Talking metadata: Understanding privacy implications of volunteer contributions in citizen science projects

Citizen science (CS) projects typically have citizen scientists with different levels of expertise and agency contributing data or knowledge. Every contribution leaves traces of their involvement, including metadata such as locations or emails. Through four case studies this paper explores the generation, use, and publication practices of CS projects’ metadata. We use a mixed-method approach combining document reviews, interviews, and an online survey, to generate insights into current metadata practices and perceptions of project contributors and organisers. We identify several weaknesses in CS projects’ data collection practices: Participants have only limited awareness of the metadata they contribute, and the privacy implications it can have. Matching expectations between project contributors and organisers regarding acknowledgement is crucial - and metadata play a key role. Projects need data processes and documentation aligned with open science principles, and clear communication to contributors about the data they collect and use. Finally, projects need to consider the mental models of contributors in relation to personal data and associated risks. We derive key considerations that data-intensive CS projects should make in their initial design phase, to generate consistent metadata in line with their participants’ expectations, which in turn increases transparency and thus can increase data reuse

What is the message? Perspectives on Visual Data Communication

Data visualizations are used to communicate messages to diverse audiences. It is unclear whether interpretations of these visualizations match the messages their creators aim to convey. In a mixed-methods study, we investigate how data in the popular science magazine Scientific American are visually communicated and understood. We first analyze visualizations about climate change and pandemics published in the magazine over a fifty-year period. Acting as chart readers, we then interpret visualizations with and without textual elements, identifying takeaway messages and creating field notes. Finally, we compare a sample of our interpreted messages to the intended messages of chart producers, drawing on interviews conducted with magazine staff. These data allow us to explore understanding visualizations through three perspectives: that of the charts, visualization readers, and visualization producers. Building on our findings from a thematic analysis, we present in-depth insights into data visualization sensemaking, particularly regarding the role of messages and textual elements; we propose a message typology, and we consider more broadly how messages can be conceptualized and understood

DATA:SEARCH'18 -- Searching Data on the Web

This half day workshop explores challenges in data search, with a particular focus on data on the web. We want to stimulate an interdisciplinary discussion around how to improve the description, discovery, ranking and presentation of structured and semi-structured data, across data formats and domain applications. We welcome contributions describing algorithms and systems, as well as frameworks and studies in human data interaction. The workshop aims to bring together communities interested in making the web of data more discoverable, easier to search and more user friendly

Plus size and inclusivity in design

The number of people classed as ‘plus size’, meaning overweight or obese is increasing and many feel excluded from every day activities and environments. Whilst the need to cater for a diverse population in terms of anthropometry is recognized, rarely are the emotional and social aspects of interactions with design/environments taken into account. This paper presents the findings of an interview study to explore barriers affecting plus size people with regard to inclusivity in their lives. The findings suggest that emotional responses to physical issues are important to consider and will contribute to recommendations to raise awareness and facilitate empathy