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As machine learning and algorithmic decision making systems are increasingly being leveraged in high-stakes human-in-the-loop settings, there is a pressing need to understand the rationale of their predictions.
An evaluation of the human-interpretability of explanation
I. Lage, E. Chen, J. He, M. Narayanan, B. Kim, S. Gershman, and F. Doshi-Velez · 1902
Earlier work this paper cites.
Regularizing black-box models for improved interpretability
G. Plumb, M. Al-Shedivat, A. A. Cabrera, A. Perer, E. Xing, and A. Talwalkar · 1902
Earlier work this paper cites.
Shrinkage estimators in online experiments
D. Dimmery, E. Bakshy, and J. Sekhon · 1904
Earlier work this paper cites.
Explaining machine learning classifiers through diverse counterfactual explanations
R. K. Mothilal, A. Sharma, and C. Tan · 1905
Earlier work this paper cites.
"how do i fool you?": Manipulating user trust via misleading black box explanations
H. Lakkaraju and O. Bastani · 1911
Earlier work this paper cites.
How can we fool LIME and SHAP? adversarial attacks on post hoc explanation methods
D. Slack, S. Hilgard, E. Jia, S. Singh, and H. Lakkaraju · 1911
Earlier work this paper cites.
Hasse diagrams in statistical consulting and teaching
S. L. Lohr · 1995
Earlier work this paper cites.
General methods for monitoring convergence of iterative simulations
S. P. Brooks and A. Gelman · 1998
Earlier work this paper cites.
A visual environment for designing experiments
P. L. Darius, W. J. Coucke, and K. M. Portier · 1998
Earlier work this paper cites.
Problems with null hypothesis significance testing (nhst): what do the textbooks say?
J. A. Gliner, N. L. Leech, and G. A. Morgan · 2002
Earlier work this paper cites.
Bayesian item response modeling: Theory and applications
J.-P. Fox · 2010
Earlier work this paper cites.
Rethinking statistical analysis methods for chi
M. Kaptein and J. Robertson · 2012
Earlier work this paper cites.
Conducting behavioral research on amazon’s mechanical turk
W. Mason and S. Suri · 2012
Earlier work this paper cites.
Practical bayesian model evaluation using leave-one-out cross-validation and waic
A. Vehtari, A. Gelman, and J. Gabry · 2012
Earlier work this paper cites.
R: A language and environment for statistical computing, 2013
R. C. Team et al · 2013
Earlier work this paper cites.
The new statistics: Why and how
G. Cumming · 2014
Earlier work this paper cites.
Ways of Knowing in HCI , volume 2
J. S. Olson and W. A. Kellogg · 2014
Cited alongside, same era.
Controlling the false discovery rate via knockoffs
R. F. Barber, E. J. Candès, et al · 2015
Cited alongside, same era.
Social, behavioral, and economic sciences perspectives on robust and reliable science
J. T. Cacioppo, R. M. Kaplan, J. A. Krosnick, J. L. Olds, and H. Dean · 2015
Cited alongside, same era.
The extent and consequences of p-hacking in science
M. L. Head, L. Holman, R. Lanfear, A. T. Kahn, and M. D. Jennions · 2015
Cited alongside, same era.
Panning for gold: Model-x knockoffs for high-dimensional controlled variable selection
E. Candes, Y. Fan, L. Janson, and J. Lv · 2016
Cited alongside, same era.
The statistical crisis in science
A. Gelman and E. Loken · 2016
Explaining a black-box using deep variational information bottleneck approach, 2019
S. Bang, P. Xie, H. Lee, W. Wu, and E. Xing · 2019
Later among the works it cites.
Human-centered tools for coping with imperfect algorithms during medical decision-making
C. J. Cai, E. Reif, N. Hegde, J. Hipp, B. Kim, D. Smilkov, M. Wattenberg, F. Viegas, G. S. Corrado, M. C. Stumpe, and others · 2019
Later among the works it cites.
Ai now 2019 report
K. Crawford, R. Dobbe, T. Dryer, G. Fried, B. Green, E. Kaziunas, A. Kak, V. Mathur, E. McElroy, A. N. Sánchez, D. Raji, J. L. Rankin, R. Richardson, J. Schultz, S. M. West, and M. Whittaker · 2019
Later among the works it cites.
A comparative study of fairness-enhancing interventions in machine learning
S. A. Friedler, C. Scheidegger, S. Venkatasubramanian, S. Choudhary, E. P. Hamilton, and D. Roth · 2019
Later among the works it cites.
Visualization in bayesian workflow
J. Gabry, D. Simpson, A. Vehtari, M. Betancourt, and A. Gelman · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46
G. D. P. Regulation · 2016
Cited alongside, same era.
brms: An R package for Bayesian multilevel models using Stan
P.-C. Bürkner · 2017
Cited alongside, same era.
Stan: A probabilistic programming language
B. Carpenter, A. Gelman, M. D. Hoffman, D. Lee, B. Goodrich, M. Betancourt, M. Brubaker, J. Guo, P. Li, and A. Riddell · 2017
Cited alongside, same era.
Design and analysis of experiments
D. C. Montgomery · 2017
Cited alongside, same era.
False discoveries occur early on the lasso path
W. Su, M. Bogdan, E. Candes, et al · 2017
Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning
S. Corbett-Davies and S. Goel · 2018
Cited alongside, same era.
A. Z. Jacobs and H. Wallach · 2019
Later among the works it cites.
Lessons from archives: Strategies for collecting sociocultural data in machine learning, 2019
E. S. Jo and T. Gebru · 2019
Later among the works it cites.
Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning
H. Kaur, H. Nori, S. Jenkins, R. Caruana, H. Wallach, and J. W. Vaughan · 2019
Later among the works it cites.
Challenging common assumptions in the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, G. Raetsch, S. Gelly, B. Schölkopf, and O. Bachem · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
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Contextual explanation networks, 2020
M. Al-Shedivat, A. Dubey, and E. P. Xing · 2020
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Evaluating and aggregating feature-based model explanations, 2020
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Semantic discriminability for visual communication, 2020
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A study in rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning, 2020
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