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The Right to Explanation and the Right to be Forgotten are two important principles outlined to regulate algorithmic decision making and data usage in real-world applications.
Penalty and barrier methods for constrained optimization
Freund, R. M · 2004
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Yeh, I.-C. and Lien, C.-h · 2009
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Counterfactual explanations for machine learning: A review
Verma, S., Dickerson, J., and Hines, K · 2010
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The effect of race/ethnicity on sentencing: Examining sentence type, jail length, and prison length
Jordan, K. L. and Freiburger, T. L · 2014
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Towards making systems forget with machine unlearning
Cao, Y. and Yang, J · 2015
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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/ec (general data protection regulation) (text with eea relevance), May 2016
GDPR · 2016
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” why should i trust you?” explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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Understanding black-box predictions via influence functions
Koh, P. W. and Liang, P · 2017
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Inverse classification for comparison-based interpretability in machine learning
Laugel, T., Lesot, M.-J., Marsala, C., Renard, X., and Detyniecki, M · 2017
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Automatic differentiation in PyTorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Interpretable predictions of tree-based ensembles via actionable feature tweaking
Tolomei, G., Silvestri, F., Haines, A., and Lalmas, M · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Wachter, S., Mittelstadt, B., and Russell, C · 2017
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California consumer privacy act (ccpa), 2018
CCPA · 2018
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Counterfactual explanations without opening the black box: automated decisions and the gdpr
Wachter, S., Mittelstadt, B., and Russell, C · 2018
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Making ai forget you: Data deletion in machine learning
Ginart, A., Guan, M., Valiant, G., and Zou, J. Y · 2019
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A swiss army infinitesimal jackknife
Giordano, R., Stephenson, W., Liu, R., Jordan, M., and Broderick, T · 2019
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Preserving causal constraints in counterfactual explanations for machine learning classifiers
Model-agnostic counterfactual explanations for consequential decisions
Karimi, A.-H., Barthe, G., Balle, 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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Learning model-agnostic counterfactual explanations for tabular data
Pawelczyk, M., Broelemann, K., and Kasneci, G · 2020
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Interpretable and interactive summaries ofactionable recourses
Rawal, K. and Lakkaraju, H · 2020
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Machine unlearning
Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N · 2021
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Approximate data deletion from machine learning models
Izzo, Z., Anne Smart, M., Chaudhuri, K., and Zou, J · 2021
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Mahajan, D., Tan, C., and Sharma, A · 2019
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Actionable recourse in linear classification
Ustun, B., Spangher, A., and Liu, Y · 2019
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Interpretable counterfactual explanations guided by prototypes
Van Looveren, A. and Klaise, J · 2019
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An automatic Finite-Sample robustness metric: When can dropping a little data make a big difference?
Broderick, T., Giordano, R., and Meager, R · 2020
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Multi-objective counterfactual explanations
Dandl, S., Molnar, C., Binder, M., and Bischl, B · 2020
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Formalizing data deletion in the context of the right to be forgotten
Garg, S., Goldwasser, S., and Vasudevan, P. N · 2020
Cited alongside, same era.
Certified data removal from machine learning models
Guo, C., Goldstein, T., Hannun, A., and Van Der Maaten, L · 2020
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Descent-to-Delete: Gradient-Based methods for machine unlearning
Neel, S., Roth, A., and Sharifi-Malvajerdi, S · 2021
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Carla: A python library to benchmark algorithmic recourse and counterfactual explanation algorithms
Pawelczyk, M., Bielawski, S., Van den Heuvel, J., Richter, T., and Kasneci, G · 2021
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Algorithmic recourse in the wild: Understanding the impact of data and model shifts
Rawal, K., Kamar, E., and Lakkaraju, H · 2021
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Towards robust and reliable algorithmic recourse
Upadhyay, S., Joshi, S., and Lakkaraju, H · 2021
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Efficient training of Low-Curvature neural networks
Srinivas, S., Matoba, K., Lakkaraju, H., and Fleuret, F · 2022
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