A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
Title resolution pending
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
roles
background 3polarities
background 3representative citing papers
Safire is a two-dimensional conceptual model that defines visualization similarity via comparison criteria and representation modalities to support retrieval system design and analysis.
BONSAI introduces a four-layer architecture and four-phase workflow for human-AI co-development of visual analytics applications, shown in case studies to enable efficient novel tool creation and reconstruction from paper descriptions.
Introduces progressive visualization for comparing causal discovery algorithms and comparative graph layouts for analyzing multi-outcome causal graphs in healthcare.
The UK Co-Benefits Atlas design process yields a conceptual framework of five driving forces—data, people, stories, context, and the atlas itself—that shape visualization atlas creation at different stages.
citing papers explorer
-
What Should Explanations Contain? A Human-Centered Explanation Content Model for Local, Post-Hoc Explanations
A 14-code content model for local post-hoc AI explanations, derived from 325 user statements and validated by experts with high reliability scores.
-
Safire: Similarity Framework for Visualization Retrieval
Safire is a two-dimensional conceptual model that defines visualization similarity via comparison criteria and representation modalities to support retrieval system design and analysis.
-
BONSAI: A Mixed-Initiative Workspace for Human-AI Co-Development of Visual Analytics Applications
BONSAI introduces a four-layer architecture and four-phase workflow for human-AI co-development of visual analytics applications, shown in case studies to enable efficient novel tool creation and reconstruction from paper descriptions.
-
Visual Analysis of Multi-outcome Causal Graphs
Introduces progressive visualization for comparing causal discovery algorithms and comparative graph layouts for analyzing multi-outcome causal graphs in healthcare.
-
Designing a Visualization Atlas: Lessons & Reflections from The UK Co-Benefits Atlas for Climate Mitigation
The UK Co-Benefits Atlas design process yields a conceptual framework of five driving forces—data, people, stories, context, and the atlas itself—that shape visualization atlas creation at different stages.