Abstract
In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making.
| Original language | English |
|---|---|
| Pages (from-to) | 166-180 |
| Number of pages | 15 |
| Journal | Visual Informatics |
| Volume | 2 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2018 Sep 1 |
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Keywords
- Guidance
- Model evaluation
- Visual analytics
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Human-Computer Interaction
- Software
Cite this
Guidance in the human–machine analytics process. / Collins, Christopher; Andrienko, Natalia; Schreck, Tobias; Yang, Jing; Choo, Jaegul; Engelke, Ulrich; Jena, Amit; Dwyer, Tim.
In: Visual Informatics, Vol. 2, No. 3, 01.09.2018, p. 166-180.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Guidance in the human–machine analytics process
AU - Collins, Christopher
AU - Andrienko, Natalia
AU - Schreck, Tobias
AU - Yang, Jing
AU - Choo, Jaegul
AU - Engelke, Ulrich
AU - Jena, Amit
AU - Dwyer, Tim
PY - 2018/9/1
Y1 - 2018/9/1
N2 - In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making.
AB - In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making.
KW - Guidance
KW - Model evaluation
KW - Visual analytics
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U2 - 10.1016/j.visinf.2018.09.003
DO - 10.1016/j.visinf.2018.09.003
M3 - Article
VL - 2
SP - 166
EP - 180
JO - Visual Informatics
JF - Visual Informatics
SN - 2543-2656
IS - 3
ER -