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As machine learning models are increasingly used in critical decision-making settings (e.g., healthcare, finance), there has been a growing emphasis on developing methods to explain model predictions.
Certifying and removing disparate impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S · 2015
Earlier work this paper cites.
Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
Letham, B., Rudin, C., McCormick, T. H., and Madigan, D · 2015
Earlier work this paper cites.
Machine bias
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
Earlier work this paper cites.
Gould, S., Fernando, B., Cherian, A., Anderson, P., Santa Cruz, R., and Guo, E · 2016
Earlier work this paper cites.
Interpretable decision sets: A joint framework for description and prediction
Lakkaraju, H., Bach, S. H., and Leskovec, J · 2016
Earlier work this paper cites.
”why should I trust you?”: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Earlier work this paper cites.
SmoothGrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F. B., and Wattenberg, M · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
Cited alongside, same era.
On the robustness of interpretability methods
Alvarez-Melis, D. and Jaakkola, T. S · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Wachter, S., Mittelstadt, B., and Russell, C · 2018
Equalizing recourse across groups
Gupta, V., Nokhiz, P., Dutta Roy, C., and Venkatasubramanian, S · 2019
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Interpretable Counterfactual Explanations Guided by Prototypes
Van Looveren, A. and Klaise, J · 2019
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Explainable machine learning in deployment
Bhatt, U., Xiang, A., Sharma, S., Weller, A., Taly, A., Jia, Y., Ghosh, J., Puri, R., Moura, J. M. F., and Eckersley, P · 2020
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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects
Karimi, A.-H., Barthe, G., Schölkopf, B., and Valera, I · 2020
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Explaining machine learning classifiers through diverse counterfactual explanations
Mothilal, R. K., Sharma, A., and Tan, C · 2020
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Cited alongside, same era.
This looks like that: Deep learning for interpretable image recognition
Chen, C., Li, O., Tao, D., Barnett, A., Rudin, C., and Su, J. K · 2019
Cited alongside, same era.
”why should i trust you?”: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C
Cited in the paper.
The philosophical basis of algorithmic recourse
Venkatasubramanian, S. and Alfano, M · 2020
Later among the works it cites.