Fetching the paper…
Reading the bibliography…
Whilst an abundance of techniques have recently been proposed to generate counterfactual explanations for the predictions of opaque black-box systems, markedly less attention has been paid to exploring the uncertainty of these generated explanations.
Lof: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., and Sander, J · 2000
Earlier work this paper cites.
A case-based explanation system for black-box systems
Nugent, C. and Cunningham, P · 2005
Earlier work this paper cites.
MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
Earlier work this paper cites.
Uncertainty in deep learning
Gal, Y · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Earlier work this paper cites.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
Earlier work this paper cites.
What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
Earlier work this paper cites.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Earlier work this paper cites.
Reliable decision support using counterfactual models
Schulam, P. and Saria, S · 2017
Earlier work this paper cites.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Wachter, S., Mittelstadt, B., and Russell, C · 2017
Earlier work this paper cites.
Peeking inside the black-box: a survey on explainable artificial intelligence (xai)
Adadi, A. and Berrada, M · 2018
Earlier work this paper cites.
Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Dhurandhar, A., Chen, P.-Y., Luss, R., Tu, C.-C., Ting, P., Shanmugam, K., and Das, P · 2018
Earlier work this paper cites.
To trust or not to trust a classifier
Jiang, H., Kim, B., Guan, M. Y., and Gupta, M. R · 2018
Earlier work this paper cites.
Counterfactuals in explainable artificial intelligence (XAI): Evidence from human reasoning
Byrne, R. M · 2019
Earlier work this paper cites.
Counterfactual visual explanations
Goyal, Y., Wu, Z., Ernst, J., Batra, D., Parikh, D., and Lee, S · 2019
Cited alongside, same era.
The dangers of post-hoc interpretability: Unjustified counterfactual explanations
Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., and Detyniecki, M · 2019
Cited alongside, same era.
Preserving causal constraints in counterfactual explanations for machine learning classifiers
Mahajan, D., Tan, C., and Sharma, A · 2019
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
Miller, T · 2019
Cited alongside, same era.
Do deep generative models know what they don’t know?
Nalisnick, E., Matsukawa, A., Teh, Y. W., Gorur, D., and Lakshminarayanan, B · 2019
Cited alongside, same era.
Can you trust your model's uncertainty? evaluating predictive uncertainty under dataset shift
Model-agnostic counterfactual explanations for consequential decisions
Karimi, A.-H., Barthe, G., Balle, B., and Valera, I · 2020
Later among the works it cites.
Good counterfactuals and where to find them: A case-based technique for generating counterfactuals for explainable ai (xai)
Keane, M. T. and Smyth, B · 2020
Later among the works it cites.
Alibi: Algorithms for monitoring and explaining machine learning models
Klaise, J., Van Looveren, A., Vacanti, G., and Coca, A · 2020
Later among the works it cites.
Why does my model fail? contrastive local explanations for retail forecasting
Lucic, A., Haned, H., and de Rijke, M · 2020
Later among the works it cites.
McGrath, S., Mehta, P., Zytek, A., Lage, I., and Lakkaraju, H · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B., and Snoek, J · 2019
Cited alongside, same era.
How model accuracy and explanation fidelity influence user trust
Papenmeier, A., Englebienne, G., and Seifert, C · 2019
Cited alongside, same era.
Failing loudly: An empirical study of methods for detecting dataset shift
Rabanser, S., Günnemann, S., and Lipton, Z. C · 2019
Cited alongside, same era.
Actionable recourse in linear classification
Ustun, B., Spangher, A., and Liu, Y · 2019
Cited alongside, same era.
Interpretable counterfactual explanations guided by prototypes
Van Looveren, A. and Klaise, J · 2019
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2019
Cited alongside, same era.
Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty
Bhatt, U., Antorán, J., Zhang, Y., Liao, Q. V., Sattigeri, P., Fogliato, R., Melançon, G. G., Krishnan, R., Stanley, J., Tickoo, O., et al · 2020
Cited alongside, same era.
Interpretable machine learning – a brief history, state-of-the-art and challenges
Molnar, C., Casalicchio, G., and Bischl, B · 2020
Later among the works it cites.
Face: feasible and actionable counterfactual explanations
Poyiadzi, R., Sokol, K., Santos-Rodriguez, R., De Bie, T., and Flach, P · 2020
Later among the works it cites.
Can i still trust you?: Understanding the impact of distribution shifts on algorithmic recourses
Rawal, K., Kamar, E., and Lakkaraju, H · 2020
Later among the works it cites.
de Bie, K., Lucic, A., and Haned, H · 2021
Closest in time.
Instance-based counterfactual explanations for time series classification
Delaney, E., Greene, D., and Keane, M. T · 2021
Closest in time.
Algorithmic recourse: from counterfactual explanations to interventions
Karimi, A.-H., Schölkopf, B., and Valera, I · 2021
Closest in time.
If only we had better counterfactual explanations: Five key deficits to rectify in the evaluation of counterfactual xai techniques
Keane, M. T., Kenny, E. M., Delaney, E., and Smyth, B · 2021
Closest in time.
On generating plausible counterfactual and semi-factual explanations for deep learning
Kenny, E. M. and Keane, M. T · 2021
Closest in time.
Generating interpretable counterfactual explanations by implicit minimisation of epistemic and aleatoric uncertainties
Schut, L., Key, O., Mc Grath, R., Costabello, L., Sacaleanu, B., Gal, Y., et al · 2021
Closest in time.
Towards robust and reliable algorithmic recourse
Upadhyay, S., Joshi, S., and Lakkaraju, H · 2021
Closest in time.