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This paper systematically derives design dimensions for the structured evaluation of explainable artificial intelligence (XAI) approaches.
FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning
A. A. Cabrera, W. Epperson, F. Hohman, M. Kahng, J. Morgenstern, and D. H. Chau · 1904
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F. Hohman, H. Park, C. Robinson, and D. H. Chau · 1904
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The mindlessness of ostensibly thoughtful action: The role of "placebic" information in interpersonal interaction
E. J. Langer, A. Blank, and B. Chanowitz · 1939
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Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems
B. M. Muir · 1994
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Effects of task difficulty on use of advice
F. Gino and D. A. Moore · 2007
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The Psychology (and Economics) of Trust
A. M. Evans and J. I. Krueger · 2009
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Effects of Influence on User Trust in Predictive Decision Making
J. Zhou, Z. Li, H. Hu, K. Yu, F. Chen, Z. Li, and Y. Wang · 2009
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The Influences of Message and Source Factors on Advice Outcomes
B. Feng and E. L. MacGeorge · 2010
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Chapter five - The Dunning–Kruger Effect: On Being Ignorant of One’s Own Ignorance
D. Dunning · 2011
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A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction
P. A. Hancock, D. R. Billings, K. E. Schaefer, J. Y. C. Chen, E. J. de Visser, and R. Parasuraman · 2011
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FeatureInsight: Visual support for error-driven feature ideation in text classification
M. Brooks, S. Amershi, B. Lee, S. M. Drucker, A. Kapoor, and P. Simard · 2015
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Probing Projections: Interaction Techniques for Interpreting Arrangements and Errors of Dimensionality Reductions
J. Stahnke, M. Dörk, B. Müller, and A. Thom · 2015
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Feature-based visual exploration of text classification
F. Stoffel, L. Flekova, D. Oelke, I. Gurevych, and D. A. Keim · 2015
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Crowd-Based Personalized Natural Language Explanations for Recommendations
S. Chang, F. M. Harper, and L. G. Terveen · 2016
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How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface
R. F. Kizilcec · 2016
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Towards Better Analysis of Deep Convolutional Neural Networks
M. Liu, J. Shi, Z. Li, C. Li, J. Zhu, and S. Liu · 2016
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ExpLOD: A Framework for Explaining Recommendations Based on the Linked Open Data Cloud
C. Musto, F. Narducci, P. Lops, M. De Gemmis, and G. Semeraro · 2016
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Approximated and User Steerable tSNE for Progressive Visual Analytics
N. Pezzotti, B. P. F. Lelieveldt, L. v. d. Maaten, T. Höllt, E. Eisemann, and A. Vilanova · 2016
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Squares: Supporting Interactive Performance Analysis for Multiclass Classifiers
D. Ren, S. Amershi, B. Lee, J. Suh, and J. D. Williams · 2016
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What is Trust? A Multidisciplinary Review, Critique, and Synthesis
B. G. Robbins · 2016
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A Meta-Analysis of Factors Influencing the Development of Trust in Automation: Implications for Understanding Autonomy in Future Systems
K. E. Schaefer, J. Y. C. Chen, J. L. Szalma, and P. A. Hancock · 2016
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Towards A Rigorous Science of Interpretable Machine Learning
F. Doshi-Velez and B. Kim · 2017
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Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework
M. El-Assady, R. Sevastjanova, F. Sperrle, D. Keim, and C. Collins · 2017
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ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
M. Kahng, P. Y. Andrews, A. Kalro, and D. H. Chau · 2017
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A Workflow for Visual Diagnostics of Binary Classifiers using Instance-Level Explanations
J. Krause, A. Dasgupta, J. Swartz, Y. Aphinyanaphongs, and E. Bertini · 2017
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Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses
A. Kumpf, B. Tost, M. Baumgart, M. Riemer, R. Westermann, and M. Rautenhaus · 2017
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Clustervision: Visual Supervision of Unsupervised Clustering
B. C. Kwon, B. Eysenbach, J. Verma, K. Ng, C. De Filippi, W. F. Stewart, and A. Perer · 2017
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RCLens: Interactive Rare Category Exploration and Identification
H. Lin, S. Gao, D. Gotz, F. Du, J. He, and N. Cao · 2017
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Understanding the Relationship Between Interactive Optimisation and Visual Analytics in the Context of Prostate Brachytherapy
J. Liu, T. Dwyer, K. Marriott, J. Millar, and A. Haworth · 2017
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Analyzing the Training Processes of Deep Generative Models
M. Liu, J. Shi, K. Cao, J. Zhu, and S. Liu · 2017
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Visual Exploration of Semantic Relationships in Neural Word Embeddings
RuleMatrix: Visualizing and Understanding Classifiers with Rules
Y. Ming, H. Qu, and E. Bertini · 2018
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Manipulating and Measuring Model Interpretability
F. Poursabzi-Sangdeh, D. G. Goldstein, J. M. Hofman, J. W. Vaughan, and H. Wallach · 2018
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Explanations As Mechanisms for Supporting Algorithmic Transparency
E. Rader, K. Cotter, and J. Cho · 2018
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Seq2seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models
H. Strobelt, S. Gehrmann, M. Behrisch, A. Perer, H. Pfister, and A. M. Rush · 2018
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DQNViz: A Visual Analytics Approach to Understand Deep Q-Networks
J. Wang, L. Gou, H.-W. Shen, and H. Yang · 2018
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S. Liu, P. Bremer, J. J. Thiagarajan, V. Srikumar, B. Wang, Y. Livnat, and V. Pascucci · 2017
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Visual Diagnosis of Tree Boosting Methods
S. Liu, J. Xiao, J. Liu, X. Wang, J. Wu, and J. Zhu · 2017
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T. Miller, P. Howe, and L. Sonenberg · 2017
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Understanding Hidden Memories of Recurrent Neural Networks
Y. Ming, S. Cao, R. Zhang, Z. Li, Y. Chen, Y. Song, and H. Qu · 2017
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TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees
T. Muhlbacher, L. Linhardt, T. Moller, and H. Piringer · 2017
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SOMFlow: Guided Exploratory Cluster Analysis with Self-Organizing Maps and Analytic Provenance
D. Sacha, M. Kraus, J. Bernard, M. Behrisch, T. Schreck, Y. Asano, and D. A. Keim · 2017
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LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
H. Strobelt, S. Gehrmann, H. Pfister, and A. M. Rush · 2017
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J. Zhang, Y. Wang, P. Molino, L. Li, and D. S. Ebert · 2018
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iForest: Interpreting Random Forests via Visual Analytics
X. Zhao, Y. Wu, D. L. Lee, and W. Cui · 2018
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The Effects of Example-based Explanations in a Machine Learning Interface
C. J. Cai, J. Jongejan, and J. Holbrook · 2019
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Explaining Decision-Making Algorithms Through UI: Strategies to Help Non-Expert Stakeholders
H.-F. Cheng, R. Wang, Z. Zhang, F. O’Connell, T. Gray, F. M. Harper, and H. Zhu · 2019
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Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
J. Dodge, Q. V. Liao, Y. Zhang, R. K. E. Bellamy, and C. Dugan · 2019
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The Effect of Explanations and Algorithmic Accuracy on Visual Recommender Systems of Artistic Images
V. Dominguez, P. Messina, I. Donoso-Guzmán, and D. Parra · 2019
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The Impact of Placebic Explanations on Trust in Intelligent Systems
M. Eiband, D. Buschek, A. Kremer, and H. Hussmann · 2019
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Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections
M. El-Assady, R. Kehlbeck, C. Collins, D. Keim, and O. Deussen · 2019
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Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models
F. Hohman, A. Head, R. Caruana, R. DeLine, and S. M. Drucker · 2019
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Personalized Explanations for Hybrid Recommender Systems
P. Kouki, J. Schaffer, J. Pujara, J. O’Donovan, and L. Getoor · 2019
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Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics
Y. Ma, T. Xie, J. Li, and R. Maciejewski · 2019
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To Explain or Not to Explain: The Effects of Personal Characteristics when Explaining Music Recommendations
M. Millecamp, N. N. Htun, C. Conati, and K. Verbert · 2019
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Effects of the Source of Advice and Decision Task on Decisions to Request Expert Advice
R. M. Richter, M. J. Valladares, and S. C. Sutherland · 2019
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I Can Do Better Than Your AI: Expertise and Explanations
J. Schaffer, J. O’Donovan, J. Michaelis, A. Raglin, and T. Höllerer · 2019
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Shall we play? extending the visual analytics design space through gameful design concepts
R. Sevastjanova, H. Schäfer, J. Bernard, D. Keim, and M. El-Assady · 2019
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explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
T. Spinner, U. Schlegel, H. Schäfer, and M. El-Assady · 2019
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Progressive Disclosure: Empirically Motivated Approaches to Designing Effective Transparency
A. Springer and S. Whittaker · 2019
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Designing Theory-Driven User-Centric Explainable AI
D. Wang, Q. Yang, A. Abdul, and B. Y. Lim · 2019
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
M. Yin, J. Wortman Vaughan, and H. Wallach · 2019
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