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Ann Sherlock

dblp:298/4804 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › explainable AI › explainable machine learning
explanation visualization
0.612022
Explanatory Journeys: Visualising to Understand and Explain Administrative Justice Paths of Redress · IEEE Trans. Vis. Comput. Graph. 2022
Visualization and visual analytics
visual analytics
0.612022
Explanatory Journeys: Visualising to Understand and Explain Administrative Justice Paths of Redress · IEEE Trans. Vis. Comput. Graph. 2022

Methods — techniques the papers use, named apart from their topics

heuristic evaluation · 1.1expert workshop · 1.1
YearPublicationVenuePosition
2022 Explanatory Journeys: Visualising to Understand and Explain Administrative Justice Paths of Redress
abstract
Administrative justice concerns the relationships between individuals and the state. It includes redress and complaints on decisions of a child's education, social care, licensing, planning, environment, housing and homelessness. However, if someone has a complaint or an issue, it is challenging for people to understand different possible redress paths and explore what path is suitable for their situation. Explanatory visualisation has the potential to display these paths of redress in a clear way, such that people can see, understand and explore their options. The visualisation challenge is further complicated because information is spread across many documents, laws, guidance and policies and requires judicial interpretation. Consequently, there is not a single database of paths of redress. In this work we present how we have co-designed a system to visualise administrative justice paths of redress. Simultaneously, we classify, collate and organise the underpinning data, from expert workshops, heuristic evaluation and expert critical reflection. We make four contributions: (i) an application design study of the explanatory visualisation tool (Artemus), (ii) coordinated and co-design approach to aggregating the data, (iii) two in-depth case studies in housing and education demonstrating explanatory paths of redress in administrative law, and (iv) reflections on the expert co-design process and expert data gathering and explanatory visualisation for administrative justice and law.
Jonathan Roberts 0002, Peter W. S. Butcher, Ann Sherlock, Sarah Nason
IEEE Trans. Vis. Comput. Graph.3