63 research outputs found
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
Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions
Explanations of AI systems rarely address the information needs of people
affected by algorithmic decision-making (ADM). This gap between conveyed
information and information that matters to affected stakeholders can impede
understanding and adherence to regulatory frameworks such as the AI Act. To
address this gap, we present the "XAI Novice Question Bank": A catalog of
affected stakeholders' information needs in two ADM use cases (employment
prediction and health monitoring), covering the categories data, system
context, system usage, and system specifications. Information needs were
gathered in an interview study where participants received explanations in
response to their inquiries. Participants further reported their understanding
and decision confidence, showing that while confidence tended to increase after
receiving explanations, participants also met understanding challenges, such as
being unable to tell why their understanding felt incomplete. Explanations
further influenced participants' perceptions of the systems' risks and
benefits, which they confirmed or changed depending on the use case. When risks
were perceived as high, participants expressed particular interest in
explanations about intention, such as why and to what end a system was put in
place. With this work, we aim to support the inclusion of affected stakeholders
into explainability by contributing an overview of information and challenges
relevant to them when deciding on the adoption of ADM systems. We close by
summarizing our findings in a list of six key implications that inform the
design of future explanations for affected stakeholder audiences.Comment: Main text: 21 pages, 3 figures. Supplementary material is provided.
Manuscript submitted for review to IJHC
"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
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
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
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
- …