/Timeline

min
-
1
+

/Keywords

study type:
observation study30
lab experiment26
pair analytics9
crowdsourcing6
result processing:
qualitative44
both20
quantitative7
learning phase:
walkthrough29
none19
training10
unguided exploration9
N/A8
domain/dataset expertise:
high29
28
mid7
low5
study condition2
ml/ai expertise:
28
high20
mid12
study condition6
low5
gender:
N/A48
reported23
participants:
1-944
10-1911
20-408
>404
n/a4
analysis task:
understand30
explore26
refine24
use23
diagnose18
compare15
hypothesize7
justify7
train4
data type:
multivariate data38
text data16
images11
video4
geo1
N/A1
model quality:
N/A39
measured13
motivated8
measured condition2
study condition2
observed model quality:
N/A53
measured6
motivated6
measured condition5
study condition1
model transparency:
N/A55
motivated13
measured2
measured condition1
model trustworthiness:
N/A51
motivated12
measured7
measured condition1
model interpretability:
N/A36
motivated22
measured9
measured condition2
study condition2
model controllability:
N/A53
motivated14
measured2
study condition2
explanation transparency:
N/A67
measured2
motivated2
explanation trustworthiness:
N/A64
measured4
motivated2
measured condition1
explanation effectiveness:
N/A56
motivated7
measured6
measured condition2
explanation fidelity:
N/A68
motivated2
measured condition1
direct/indirect interaction:
direct48
indirect31
N/A5
interaction phase:
post training42
training26
N/A4
data preprocessing3
data selection2
guidance degree:
N/A48
orienting15
directing7
prescribing3
guidance knowledge gap:
N/A49
data12
task9
va method5
guidance adaptation:
N/A62
content7
context2
other:
min
-
1
+

/Authors

El-Assady, M.6
Liu, S.6
Ming, Y.5
Qu, H.5
Collins, C.4
Gou, L.4
Keim, D.4
Pfister, H.4
Zhu, J.4
Amershi, S.3
Behrisch, M.3
Kwon, B. C.3
Lee, B.3
Li, Z.3
Liu, M.3
Ng, K.3
Schreck, T.3
Sevastjanova, R.3
Shen, H.3
Sperrle, F.3
Wang, J.3
Yang, H.3
Bernard, J.2
Cao, K.2
Chen, C.2
Chen, J.2
Chen, M.2
Chen, S.2
Cheng, F.2
Das, S.2
Deussen, O.2
Dörk, M.2
Endert, A.2
Guzdial, M.2
Heimerl, F.2
Kehlbeck, R.2
Lee, J.2
Li, Y.2
Liao, N.2
Liu, Y.2
Ma, Y.2
Maciejewski, R.2
Mühlbacher, T.2
Müller, B.2
Perer, A.2
Piringer, H.2
Ren, D.2
Ren, L.2
Reno, J.2
Riedl, Mark O.2
Saket, B.2
Shah, S.2
Shah, V.2
Shi, J.2
Smilkov, D.2
Smith, G.2
Stahnke, J.2
Su, H.2
Suh, J.2
Thom, A.2
Wang, X.2
Wattenberg, M.2
Williams, J. D.2
Xie, T.2
Zhang, Z.2
Abdul-Rahman, A.1
Alqaraawi, A.1
Anand, V.1
Andrews, P. Y.1
Asano, Y.1
Bedi, R.1
Bertini, E.1
Beyer, J.1
Birchfield, M.1
Boisvert, J.1
Bosch, H.1
Bouwman, A. R. A.1
Boyd-Graber, J.1
Brazil, E.1
Bremer, P.1
Brooks, M.1
Brudno, M.1
Bryan, C. J.1
Burggraaff, J.1
Cai, Carrie J.1
Cao, N.1
Cao, S.1
Caruana, R.1
Cashman, D.1
Cavallo, M.1
Chang, R.1
Chau, D. H.1
Chen, W.1
Chen, X.1
Chen, Y.1
Cheng, H.1
Chevalier, F.1
Choo, J.1
Coninx, K.1
Cook, K.1
Coppers, S.1
Corrado, Greg S.1
Costanza, E.1
Cramer, N.1
Cronk, N.1
Cui, W.1
D'Souza, M.1
Daniels, J.1
Dasgupta, A.1
DeLine, R.1
Demiralp, Ç1
Deng, Z.1
Dingen, D.1
Dong, F.1
Dorn, Jonas F.1
dos Santos Amorim, E.1
Drucker, S. M.1
Drucker, Steven M.1
Erel, H.1
Ertl, T.1
Eslami, M.1
Eysenbach, B.1
Fan, R.1
Fellner, D.1
Filippi, C. De1
Findlater, L.1
Fritz, D.1
Frohnert, B. I.1
Gao, Ge1
Gehlenborg, N.1
Ghosh, S.1
Gladstone, C.1
Gleicher, M.1
Gray, T.1
Gschwandtner, T.1
Gstrein, E.1
Gu, Z.1
Haehn, D.1
Harper, F. Maxwell1
Head, A.1
Hegde, N.1
Hipp, J.1
Hitron, T.1
Hofmann, M.1
Hohman, F.1
Houthuizen, P.1
Huang, L.1
Humayoun, S.1
Hutter, M.1
Jang, W.-D.1
Ji, X.1
Jin, Z.1
Joia, P.1
Kahng, M.1
Kalro, A.1
Kamm, Christian P.1
Kao, D.1
Kapoor, A.1
Karahalios, K.1
Keim, D. A.1
Kim, B.1
Kim, J.-H.1
Kim, N. W.1
Kittley-Davies, J.1
Koch, S.1
Kontschieder, P.1
Korsten, E. H. H. M.1
Kraus, M.1
Krause, J.1
Krishna Kumaran, Sneha R.1
Krishnan, D.1
Krueger, R.1
Kuntner, J.1
Lafrance, R. A.1
Lee, D. L.1
Leite, Roger A.1
Lekschas, F.1
Li, C.1
Li, J.1
Li, N.1
Li, T.1
Lin, H.1
Lindley, S.1
Linhardt, L.1
Liu, D.1
Liu, J.1
Liu, X.1
Lu, Y.1
Lundgren, M.1
Luyten, K.1
Ma, E.1
Ma, K.-L.1
Machiraju, R.1
Mané, D.1
Mestrom, E. H. J.1
Miksch, S.1
Möller, T.1
Morrison, C.1
Morrissey, R.1
Mosca, A.1
Mu, X.1
Nam, Y.1
Nonato, L.1
O'Connell, F.1
Olsen, M.1
Orlev, Y.1
Pan, R.1
Park, H.1
Park, K.1
Parvinzamir, F.1
Pascucci, V.1
Payne, S.1
Peterson, B.1
Reif, E.1
Ritter, A.1
Roe, G.1
Rogers, A.1
Rota Bulò, S.1
Rzeszotarski, Jeffrey M.1
Sacha, D.1
Sandvig, C.1
Sarkar, A.1
Schäfer, H.1
Schlegel, U.1
Sedlmair, M.1
Sellen, A.1
Severson, K. A.1
Shamir, A.1
Shekar, A. K.1
Shen, H.-W.1
Shen, Q.1
Shi, L.1
Simard, P.1
Singh, D.1
Smith-Renner, A.1
Smith, Micah J.1
Sokolov, A.1
Song, Y.1
Sorger, P. K.1
Sousa, M.1
Spinner, T.1
Srikumar, V.1
Stasko, J.1
Stein, S.1
Steinheimer, S.1
Stewart, W. F.1
Stumpe, Martin C.1
Sultanum, N.1
Sun, J.1
Sun, Z.1
Terry, M.1
Thai, M. T.1
Thompson, J.1
Tong, H.1
Van den Bergh, J.1
van der Lek-Ciudin, I.1
Vanallemeersch, T.1
Vandeghinste, V.1
Veer, M. van't1
Veeramachaneni, K.1
Verma, J.1
Viegas, F.1
Viégas, F. B.1
Wald, I.1
Walsh, L.1
Wang, Q.1
Wang, R.1
Wang, Y.1
Weld, Daniel S.1
Wendt, A.1
Wexler, J.1
Whaling, R.1
Wijk, J. van1
Wilson, J.1
Wilson, R.1
Wongsuphasawat, K.1
Wu, J.1
Wu, T.1
Wu, Y.1
Xia, M.1
Xiao, J.1
Xie, Y.1
Xu, K.1
Xu, P.1
Xu, W.1
Yan, J.1
Yang, R.1
Yen, P.-Y.1
Yuan, J.1
Yuan, S.1
Zaykov, Y.1
Zeppelzauer, M.1
Zhang, R.1
Zhang, W.1
Zhao, X.1
Zhao, Y.1
Zhu, H.1
Zou, L.1
Zuckerman, O.1

/Clusters

new clustering:
number of clusters
-
5
+
keywordsauthors
create clustering
71 publications
sorted by selector agreement and publication key
Computer Graphics Forum
(2017)
Constructive Visual Analytics for Text Similarity Detection
Abdul-Rahman, A.
Roe, G.
Olsen, M.
Gladstone, C.
Whaling, R.
Cronk, N.
Morrissey, R.
Chen, M.
study type:observation study
result processing:qualitative
learning phase:none
domain/dataset expertise:high
ml/ai expertise:
gender:N/A
participants:n/a
analysis task:use
data type:text data
model quality:N/A
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:N/A
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:indirect
interaction phase:training
guidance degree:N/A
guidance knowledge gap:N/A
guidance adaptation:N/A
IEEE Transactions on Visualization and Computer Graphics
(2020)
[Study 1] GUIRO: User-Guided Matrix Reordering
Behrisch, M.
Schreck, T.
Pfister, H.
study type:observation study
result processing:qualitative
learning phase:training
domain/dataset expertise:low
ml/ai expertise:low
gender:reported
participants:10-19
analysis task:compare
analysis task:explore
analysis task:understand
analysis task:use
data type:multivariate data
model quality:N/A
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:motivated
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:motivated
explanation fidelity:N/A
direct/indirect interaction:direct
interaction phase:N/A
guidance degree:orienting
guidance knowledge gap:data
guidance knowledge gap:va method
guidance adaptation:N/A
IEEE Transactions on Visualization and Computer Graphics
(2020)
[Study 2] GUIRO: User-Guided Matrix Reordering
Behrisch, M.
Schreck, T.
Pfister, H.
study type:observation study
result processing:qualitative
learning phase:walkthrough
domain/dataset expertise:high
ml/ai expertise:high
gender:reported
participants:1-9
analysis task:compare
analysis task:diagnose
analysis task:explore
analysis task:understand
analysis task:use
data type:multivariate data
model quality:N/A
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:motivated
model controllability:motivated
explanation transparency:motivated
explanation trustworthiness:N/A
explanation effectiveness:motivated
explanation fidelity:motivated
direct/indirect interaction:direct
interaction phase:N/A
guidance degree:orienting
guidance knowledge gap:data
guidance knowledge gap:va method
guidance adaptation:N/A
TVCG'18
(2018)
Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study
Bernard, Jurgen
Hutter, Marco
Zeppelzauer, Matthias
Fellner, Dieter
Sedlmair, Michael
study type:lab experiment
result processing:quantitative
learning phase:walkthrough
domain/dataset expertise:low
ml/ai expertise:mid
gender:reported
participants:10-19
analysis task:use
data type:multivariate data
model quality:measured
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:N/A
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:direct
direct/indirect interaction:indirect
interaction phase:data preprocessing
guidance degree:orienting
guidance knowledge gap:data
guidance adaptation:N/A
[inproceedings]
(2015)
FeatureInsight: Visual support for error-driven feature ideation in text classification
Brooks, M.
Amershi, S.
Lee, B.
Drucker, S. M.
Kapoor, A.
Simard, P.
study type:lab experiment
result processing:both
learning phase:unguided exploration
learning phase:walkthrough
domain/dataset expertise:
ml/ai expertise:mid
gender:reported
participants:20-40
analysis task:compare
analysis task:diagnose
analysis task:refine
analysis task:understand
data type:text data
model quality:measured
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:motivated
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:measured
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:direct
direct/indirect interaction:indirect
interaction phase:post training
interaction phase:training
guidance degree:N/A
guidance knowledge gap:N/A
guidance adaptation:N/A
CHI '19
(2019)
Human-Centered Tools for Coping with Imperfect Algorithms During Medical Decision-Making
Cai, Carrie J.
Reif, Emily
Hegde, Narayan
Hipp, Jason
Kim, Been
Smilkov, Daniel
Wattenberg, Martin
Viegas, Fernanda
Corrado, Greg S.
Stumpe, Martin C.
Terry, Michael
study type:lab experiment
result processing:both
learning phase:walkthrough
domain/dataset expertise:high
ml/ai expertise:
gender:N/A
participants:10-19
analysis task:diagnose
analysis task:hypothesize
analysis task:refine
data type:images
model quality:N/A
observed model quality:N/A
model transparency:motivated
model trustworthiness:measured
model interpretability:motivated
model controllability:motivated
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:direct
direct/indirect interaction:indirect
interaction phase:training
guidance degree:N/A
guidance knowledge gap:N/A
guidance adaptation:N/A
EuroVIS'19
(2019)
A User-based Visual Analytics Workflow for Exploratory Model Analysis
Cashman, Dylan
Humayoun, Shah Rukh
Heimerl, Florian
Park, Kendall
Das, Subhajit
Thompson, John
Saket, Bahador
Mosca, Abigail
Stasko, John
Endert, Alex
Gleicher, Michael
Chang, Remco
study type:lab experiment
result processing:qualitative
learning phase:walkthrough
domain/dataset expertise:high
ml/ai expertise:
gender:N/A
participants:1-9
analysis task:compare
analysis task:explore
analysis task:train
data type:N/A
model quality:N/A
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:motivated
model controllability:motivated
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:motivated
explanation fidelity:N/A
direct/indirect interaction:indirect
interaction phase:post training
interaction phase:training
guidance degree:N/A
guidance knowledge gap:N/A
guidance adaptation:N/A
TVCG'19
(2019)
Clustrophile 2: Guided Visual Clustering Analysis
Cavallo, M.
Demiralp, Ç
study type:lab experiment
result processing:both
learning phase:training
domain/dataset expertise:mid
ml/ai expertise:
gender:reported
participants:10-19
analysis task:explore
data type:multivariate data
model quality:N/A
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:N/A
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:N/A
interaction phase:N/A
guidance degree:N/A
guidance knowledge gap:N/A
guidance adaptation:N/A
IEEE Transactions on Visualization and Computer Graphics
(2020)
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples
Chen, C.
Yuan, J.
Lu, Y.
Liu, Y.
Su, H.
Yuan, S.
Liu, S.
study type:observation study
result processing:qualitative
learning phase:none
domain/dataset expertise:high
ml/ai expertise:high
gender:N/A
participants:1-9
analysis task:understand
data type:images
model quality:N/A
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:motivated
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:direct
direct/indirect interaction:indirect
interaction phase:post training
guidance degree:orienting
guidance knowledge gap:data
guidance adaptation:N/A
IEEE Transactions on Visualization and Computer Graphics
(2020)
DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models
Cheng, F.
Ming, Y.
Qu, H.
study type:lab experiment
result processing:qualitative
learning phase:walkthrough
domain/dataset expertise:mid
ml/ai expertise:mid
gender:N/A
participants:1-9
analysis task:explore
data type:multivariate data
model quality:N/A
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:N/A
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:direct
interaction phase:post training
guidance degree:N/A
guidance knowledge gap:N/A
guidance adaptation:N/A
CHI '19
(2019)
Explaining Decision-Making Algorithms Through UI: Strategies to Help Non-Expert Stakeholders
Cheng, Hao-Fei
Wang, Ruotong
Zhang, Zheng
O'Connell, Fiona
Gray, Terrance
Harper, F. Maxwell
Zhu, Haiyi
study type:crowdsourcing
result processing:quantitative
learning phase:none
domain/dataset expertise:
ml/ai expertise:study condition
gender:N/A
participants:>40
analysis task:understand
data type:multivariate data
model quality:N/A
observed model quality:N/A
model transparency:motivated
model trustworthiness:measured
model interpretability:measured
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:measured
explanation fidelity:N/A
direct/indirect interaction:indirect
interaction phase:post training
guidance degree:N/A
guidance knowledge gap:N/A
guidance adaptation:N/A
[inproceedings]
(2018)
Intellingo: An Intelligible Translation Environment
Coppers, Sven
Van den Bergh, Jan
Luyten, Kris
Coninx, Karin
van der Lek-Ciudin, Iulianna
Vanallemeersch, Tom
Vandeghinste, Vincent
study type:crowdsourcing
result processing:both
learning phase:training
domain/dataset expertise:high
ml/ai expertise:
gender:N/A
participants:20-40
analysis task:refine
analysis task:use
data type:text data
observed model quality:measured condition
model transparency:N/A
model trustworthiness:measured condition
model interpretability:measured condition
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:measured condition
explanation effectiveness:measured condition
explanation fidelity:measured condition
direct/indirect interaction:direct
interaction phase:post training
guidance degree:directing
guidance degree:orienting
guidance knowledge gap:data
guidance adaptation:content
IEEE Transactions on Visualization and Computer Graphics
(2020)
Geono-Cluster: Interactive Visual Cluster Analysis for Biologists
Das, S.
Saket, B.
Kwon, B. C.
Endert, A.
study type:observation study
result processing:qualitative
learning phase:unguided exploration
learning phase:walkthrough
domain/dataset expertise:high
ml/ai expertise:mid
gender:reported
participants:1-9
analysis task:explore
analysis task:hypothesize
data type:multivariate data
model quality:N/A
observed model quality:N/A
model transparency:motivated
model trustworthiness:N/A
model interpretability:measured
model controllability:measured
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:direct
direct/indirect interaction:indirect
interaction phase:training
guidance degree:directing
guidance knowledge gap:data
guidance adaptation:content
IEEE Transactions on Visualization and Computer Graphics
(2017)
Familiarity Vs Trust: A Comparative Study of Domain Scientists' Trust in Visual Analytics and Conventional Analysis Methods
Dasgupta, A.
Lee, J.
Wilson, R.
Lafrance, R. A.
Cramer, N.
Cook, K.
Payne, S.
study type:lab experiment
result processing:quantitative
learning phase:N/A
domain/dataset expertise:low
ml/ai expertise:low
gender:N/A
participants:20-40
analysis task:compare
analysis task:refine
analysis task:use
data type:multivariate data
model quality:measured
observed model quality:measured
model transparency:N/A
model trustworthiness:measured
model interpretability:measured
model controllability:N/A
explanation transparency:measured
explanation trustworthiness:measured
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:indirect
interaction phase:post training
guidance degree:N/A
guidance knowledge gap:N/A
guidance adaptation:N/A
IEEE Transactions on Visualization and Computer Graphics
(2019)
RegressionExplorer: Interactive Exploration of Logistic Regression Models with Subgroup Analysis
Dingen, D.
Veer, M. van't
Houthuizen, P.
Mestrom, E. H. J.
Korsten, E. H. H. M.
Bouwman, A. R. A.
Wijk, J. van
study type:observation study
result processing:qualitative
learning phase:training
domain/dataset expertise:high
ml/ai expertise:
gender:N/A
participants:n/a
analysis task:explore
analysis task:refine
analysis task:understand
analysis task:use
data type:multivariate data
model quality:N/A
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:N/A
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:motivated
explanation fidelity:N/A
direct/indirect interaction:direct
interaction phase:N/A
guidance degree:orienting
guidance knowledge gap:va method
guidance adaptation:N/A
[inproceedings]
(2012)
iLAMP: Exploring high-dimensional spacing through backward multidimensional projection
dos Santos Amorim, Elisa Portes
Brazil, Emilio Vital
Daniels, Joel
Joia, Paulo
Nonato, Luis Gustavo
Sousa, Mario Costa
study type:lab experiment
result processing:both
learning phase:none
domain/dataset expertise:
ml/ai expertise:
gender:N/A
participants:1-9
analysis task:diagnose
data type:multivariate data
model quality:measured condition
observed model quality:study condition
model transparency:N/A
model trustworthiness:N/A
model interpretability:N/A
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:direct
interaction phase:post training
guidance degree:N/A
guidance knowledge gap:N/A
guidance adaptation:N/A
IEEE Transactions on Visualization and Computer Graphics
(2018)
Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework
El-Assady, M.
Sevastjanova, R.
Sperrle, F.
Keim, D.
Collins, C.
study type:pair analytics
result processing:both
learning phase:walkthrough
domain/dataset expertise:high
ml/ai expertise:study condition
gender:N/A
participants:1-9
analysis task:refine
data type:text data
model quality:measured
observed model quality:motivated
model transparency:motivated
model trustworthiness:motivated
model interpretability:motivated
model controllability:N/A
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:indirect
interaction phase:training
guidance degree:N/A
guidance knowledge gap:N/A
guidance adaptation:N/A
IEEE Transactions on Visualization and Computer Graphics
(2020)
Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections
El-Assady, M.
Kehlbeck, R.
Collins, C.
Keim, D.
Deussen, O.
study type:pair analytics
result processing:both
learning phase:walkthrough
domain/dataset expertise:high
ml/ai expertise:
gender:N/A
participants:1-9
analysis task:refine
data type:text data
model quality:measured
observed model quality:N/A
model transparency:N/A
model trustworthiness:N/A
model interpretability:motivated
model controllability:motivated
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:direct
interaction phase:training
guidance degree:prescribing
guidance knowledge gap:task
guidance adaptation:N/A
Computer Graphics Forum
(2018)
ThreadReconstructor : Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics
El-Assady, Mennatallah
Sevastjanova, Rita
Keim, Daniel
Collins, Christopher
study type:pair analytics
result processing:qualitative
learning phase:walkthrough
domain/dataset expertise:study condition
ml/ai expertise:study condition
gender:N/A
participants:1-9
analysis task:compare
analysis task:explore
analysis task:train
analysis task:understand
analysis task:use
data type:text data
model quality:measured
observed model quality:measured condition
model transparency:N/A
model trustworthiness:motivated
model interpretability:study condition
model controllability:study condition
explanation transparency:N/A
explanation trustworthiness:motivated
explanation effectiveness:measured
explanation fidelity:N/A
direct/indirect interaction:direct
interaction phase:training
guidance degree:N/A
guidance knowledge gap:va method
guidance adaptation:N/A
IEEE Transactions on Visualization and Computer Graphics
(2019)
Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution
El-Assady, M.
Sperrle, F.
Deussen, O.
Keim, D.
Collins, C.
study type:pair analytics
result processing:qualitative
learning phase:none
domain/dataset expertise:high
ml/ai expertise:
gender:N/A
participants:n/a
analysis task:refine
data type:text data
observed model quality:measured
model transparency:motivated
model trustworthiness:motivated
model interpretability:N/A
model controllability:motivated
explanation transparency:N/A
explanation trustworthiness:N/A
explanation effectiveness:N/A
explanation fidelity:N/A
direct/indirect interaction:direct
interaction phase:training
guidance degree:orienting
guidance knowledge gap:task
guidance adaptation:N/A
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