Built on

Russell, C.: Efficient Search for Diverse Coherent Explanations. CoRR (abs/1901.04909) (2019), http://arxiv.org/abs/1901.04909
Original
1901
Earlier work this paper cites.
Karimi, A., Barthe, G., Balle, B., Valera, I.: Model-Agnostic Counterfactual Explanations for Consequential Decisions. CoRR (abs/1905.11190) (2019), http://arxiv.org/abs/1905.11190
Original
1905
Earlier work this paper cites.
Mothilal, R.K., Sharma, A., Tan, C.: Explaining Machine Learning Classifiers through Diverse Counterfactual explanations. CoRR (abs/1905.07697) (2019), http://arxiv.org/abs/1905.07697
Original
1905
Earlier work this paper cites.
Sharma, S., Henderson, J., Ghosh, J.: CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models. CoRR abs/1905.07857
Original
1905
Earlier work this paper cites.
Dhurandhar, A., Pedapati, T., Balakrishnan, A., Chen, P., Shanmugam, K., Puri, R.: Model Agnostic Contrastive Explanations for Structured Data. CoRR abs/1906.00117
Original
1906
Earlier work this paper cites.
Joshi, S., Koyejo, O., Vijitbenjaronk, W., Kim, B., Ghosh, J.: Towards Realistic Individual Recourse and Actionable Explanations in black-box decision making systems. CoRR abs/1907.09615
Original
1907
Earlier work this paper cites.
Looveren, A.V., Klaise, J.: Interpretable Counterfactual Explanations Guided by Prototypes. CoRR abs/1907.02584
Original
1907
Earlier work this paper cites.
Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viégas, F.B., Wilson, J.: The What- If Tool: Interactive Probing of Machine Learning Models. CoRR abs/1907.04135
Original
1907
Earlier work this paper cites.
Gower, J.C.: A General Coefficient of Similarity and Some of its Properties. Biometrics 27
1971
Earlier work this paper cites.
Syswerda, G.: Uniform Crossover in Genetic Algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms. p. 2–9. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1989)
1989
Earlier work this paper cites.
Deb, K., Agarwal, R.B.: Simulated Binary Crossover for Continuous Search Space. Complex Systems 9
1995
Earlier work this paper cites.
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms — a Comparative Case Ctudy. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.P. (eds.) Parallel Problem Solving from Nature — PPSN V. pp. 292–301. Springer Berlin Heidelberg, Berlin, Heidelberg (1998)
1998
Earlier work this paper cites.
Breiman, L.: Statistical Modeling: The Two Cultures. Statistical Science 16
2001
Earlier work this paper cites.

Similar

Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6
2002
Cited alongside, same era.
Avila, S.L., Krähenbühl, L., Sareni, B.: A Multi-Niching Multi-Objective Genetic Algorithm for Solving Complex Multimodal Problems. In: OIPE. Sorrento, Italy (2006), https://hal.archives-ouvertes.fr/hal-00398660
2006
Cited alongside, same era.
Li, R., Emmerich, M.T., Eggermont, J., Bäck, T., Schütz, M., Dijkstra, J., Reiber, J.H.: Mixed Integer Evolution Strategies for Parameter Optimization. Evolutionary Computation 21
2013
Cited alongside, same era.
Radulescu, A., López-Ibáñez, M., Stützle, T.: Automatically Improving the Anytime Behaviour of Multiobjective Evolutionary Algorithms. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) Evolutionary Multi-Criterion Optimization. pp. 825–840. Springer Berlin Heidelberg, Berlin, Heidelberg (2013)
2013

Then

Su, J., Vargas, D.V., Sakurai, K.: One Pixel Attack for Fooling Deep Neural Networks. IEEE Transactions on Evolutionary Computation 23
2017
Later among the works it cites.
Tolomei, G., Silvestri, F., Haines, A., Lalmas, M.: Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 465–474. KDD ’17, ACM, New York, NY, USA (2017). https://doi.org/10.1145/3097983.3098039, http://doi.acm.org/10.1145/3097983.3098039
2017
Later among the works it cites.
Wachter, S., Mittelstadt, B.D., Russell, C.: Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR. CoRR (abs/1711.00399) (2017), http://arxiv.org/abs/1711.00399
Original
2017
Later among the works it cites.
Grath, R.M., Costabello, L., Van, C.L., Sweeney, P., Kamiab, F., Shen, Z., Lécué, F.: Interpretable Credit Application Predictions With Counterfactual Explanations. CoRR (abs/1811.05245) (2018), http://arxiv.org/abs/1811.05245
Original

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2020

Multi-Objective Counterfactual Explanations

Dandl, Susanne, Molnar, Christoph, Binder, Martin et al.

Understand

Counterfactual explanations are one of the most popular methods to make predictions of black box machine learning models interpretable by providing explanations in the form of `what-if scenarios'.

Most current approaches optimize a collapsed, weighted sum of multiple objectives, which are naturally difficult to balance a-priori.
We propose the Multi-Objective Counterfactuals (MOC) method, which translates the counterfactual search into a multi-objective optimization problem.
Our approach not only returns a diverse set of counterfactuals with different trade-offs between the proposed objectives, but also maintains diversity in feature space.

Built on

Russell, C.: Efficient Search for Diverse Coherent Explanations. CoRR (abs/1901.04909) (2019), http://arxiv.org/abs/1901.04909
Original
1901
Earlier work this paper cites.
Karimi, A., Barthe, G., Balle, B., Valera, I.: Model-Agnostic Counterfactual Explanations for Consequential Decisions. CoRR (abs/1905.11190) (2019), http://arxiv.org/abs/1905.11190
Original
1905
Earlier work this paper cites.
Mothilal, R.K., Sharma, A., Tan, C.: Explaining Machine Learning Classifiers through Diverse Counterfactual explanations. CoRR (abs/1905.07697) (2019), http://arxiv.org/abs/1905.07697
Original
1905
Earlier work this paper cites.
Sharma, S., Henderson, J., Ghosh, J.: CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models. CoRR abs/1905.07857
Original
1905
Earlier work this paper cites.
Dhurandhar, A., Pedapati, T., Balakrishnan, A., Chen, P., Shanmugam, K., Puri, R.: Model Agnostic Contrastive Explanations for Structured Data. CoRR abs/1906.00117
Original
1906
Earlier work this paper cites.
Joshi, S., Koyejo, O., Vijitbenjaronk, W., Kim, B., Ghosh, J.: Towards Realistic Individual Recourse and Actionable Explanations in black-box decision making systems. CoRR abs/1907.09615
Original
1907
Earlier work this paper cites.
Looveren, A.V., Klaise, J.: Interpretable Counterfactual Explanations Guided by Prototypes. CoRR abs/1907.02584
Original
1907
Earlier work this paper cites.
Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viégas, F.B., Wilson, J.: The What- If Tool: Interactive Probing of Machine Learning Models. CoRR abs/1907.04135
Original
1907
Earlier work this paper cites.
Gower, J.C.: A General Coefficient of Similarity and Some of its Properties. Biometrics 27
1971
Earlier work this paper cites.
Syswerda, G.: Uniform Crossover in Genetic Algorithms. In: Proceedings of the 3rd International Conference on Genetic Algorithms. p. 2–9. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1989)
1989
Earlier work this paper cites.
Deb, K., Agarwal, R.B.: Simulated Binary Crossover for Continuous Search Space. Complex Systems 9
1995
Earlier work this paper cites.
Zitzler, E., Thiele, L.: Multiobjective Optimization Using Evolutionary Algorithms — a Comparative Case Ctudy. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.P. (eds.) Parallel Problem Solving from Nature — PPSN V. pp. 292–301. Springer Berlin Heidelberg, Berlin, Heidelberg (1998)
1998
Earlier work this paper cites.
Breiman, L.: Statistical Modeling: The Two Cultures. Statistical Science 16
2001
Earlier work this paper cites.

Similar

Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6
2002
Cited alongside, same era.
Avila, S.L., Krähenbühl, L., Sareni, B.: A Multi-Niching Multi-Objective Genetic Algorithm for Solving Complex Multimodal Problems. In: OIPE. Sorrento, Italy (2006), https://hal.archives-ouvertes.fr/hal-00398660
2006
Cited alongside, same era.
Li, R., Emmerich, M.T., Eggermont, J., Bäck, T., Schütz, M., Dijkstra, J., Reiber, J.H.: Mixed Integer Evolution Strategies for Parameter Optimization. Evolutionary Computation 21
2013
Cited alongside, same era.
Radulescu, A., López-Ibáñez, M., Stützle, T.: Automatically Improving the Anytime Behaviour of Multiobjective Evolutionary Algorithms. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) Evolutionary Multi-Criterion Optimization. pp. 825–840. Springer Berlin Heidelberg, Berlin, Heidelberg (2013)
2013

Then

Su, J., Vargas, D.V., Sakurai, K.: One Pixel Attack for Fooling Deep Neural Networks. IEEE Transactions on Evolutionary Computation 23
2017
Later among the works it cites.
Tolomei, G., Silvestri, F., Haines, A., Lalmas, M.: Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 465–474. KDD ’17, ACM, New York, NY, USA (2017). https://doi.org/10.1145/3097983.3098039, http://doi.acm.org/10.1145/3097983.3098039
2017
Later among the works it cites.
Wachter, S., Mittelstadt, B.D., Russell, C.: Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR. CoRR (abs/1711.00399) (2017), http://arxiv.org/abs/1711.00399
Original
2017
Later among the works it cites.
Grath, R.M., Costabello, L., Van, C.L., Sweeney, P., Kamiab, F., Shen, Z., Lécué, F.: Interpretable Credit Application Predictions With Counterfactual Explanations. CoRR (abs/1811.05245) (2018), http://arxiv.org/abs/1811.05245
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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2018