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A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs.
Investigating statistical machine learning as a tool for software development
K. Patel, J. Fogarty, J. A. Landay, and B. Harrison · 2008
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Why and why not explanations improve the intelligibility of context-aware intelligent systems
B. Y. Lim, A. K. Dey, and D. Avrahami · 2009
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Ensemblematrix: Interactive visualization to support machine learning with multiple classifiers
J. Talbot, B. Lee, A. Kapoor, and D. Tan · 2009
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Pivot viewer based visualization of information analysis
Y. Zhao · 2012
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Probabilistic reasoning in intelligent systems: networks of plausible inference
J. Pearl · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
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Modeltracker: Redesigning performance analysis tools for machine learning
S. Amershi, M. Chickering, S. Drucker, B. Lee, P. Simard, and J. Suh · 2015
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Face recognition vendor test (FRVT) performance of automated gender classification algorithms
M. Ngan · 2015
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Equality of opportunity in supervised learning
M. Hardt, E. Price, N. Srebro, et al · 2016
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
S. M. L. K. J. Angwin, J. Larson · 2016
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Interacting with predictions: Visual inspection of black-box machine learning models
J. Krause, A. Perer, and K. Ng · 2016
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”why should i trust you?”: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Visual analysis of hidden state dynamics in recurrent neural networks
H. Strobelt, S. Gehrmann, B. Huber, H. Pfister, and A. M. Rush · 2016
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Towards a rigorous science of interpretable machine learning
F. Doshi-Velez and B. Kim · 2017
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UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
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Counterfactual explanations without opening the black box: automated decisions and the gdpr
S. Wachter, B. Mittelstadt, and C. Russell · 2017
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AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias, Oct. 2018
R. K. E. Bellamy, K. Dey, M. Hind, S. C. Hoffman, S. Houde, K. Kannan, P. Lohia, J. Martino, S. Mehta, A. Mojsilovic, S. Nagar, K. N. Ramamurthy, J. Richards, D. Saha, P. Sattigeri, M. Singh, K. R. Varshney, and Y. Zhang · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory, and antiracist politics [1989]
K. Crenshaw · 2018
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Visual analytics in deep learning: An interrogative survey for the next frontiers
R. P. D. H. C. Fred Hohman, Minsuk Kahng · 2018
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J. Krause, A. Dasgupta, J. Swartz, Y. Aphinyanaphongs, and E. Bertini · 2017
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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 · 2017
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Tfx: A tensorflow-based production-scale machine learning platform
A. N. Modi, C. Y. Koo, C. Y. Foo, C. Mewald, D. M. Baylor, E. Breck, H.-T. Cheng, J. Wilkiewicz, L. Koc, L. Lew, M. A. Zinkevich, M. Wicke, M. Ispir, N. Polyzotis, N. Fiedel, S. E. Haykal, S. Whang, S. Roy, S. Ramesh, V. Jain, X. Zhang, and Z. Haque · 2017
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Smoothgrad: removing noise by adding noise, 2017
D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg · 2017
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Axiomatic attribution for deep networks, 2017
M. Sundararajan, A. Taly, and Q. Yan · 2017
Cited alongside, same era.
https://data.boston.gov/dataset/boston-police-department-fio
Bpd field interrogation and observation dataset
Cited in the paper.
https://pair-code.github.io/facets/
Facets
Cited in the paper.
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, and R. Sayres · 2018
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Rulematrix: Visualizing and understanding classifiers with rules
Y. Ming, H. Qu, and E. Bertini · 2018
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iforest: Interpreting random forests via visual analytics
X. Zhao, Y. Wu, D. Lee, and W. Cui · 2018
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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. Drucker · 2019
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Manifold: A model-agnostic framework for interpretation and diagnosis of machine learning models
J. Zhang, Y. Wang, P. Molino, L. Li, and D. S. Ebert · 2019
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