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Transparency is a fundamental requirement for decision making systems when these should be deployed in the real world.
1906
Earlier work this paper cites.
1907
Earlier work this paper cites.
1908
Earlier work this paper cites.
1909
Earlier work this paper cites.
1909
Earlier work this paper cites.
1911
Earlier work this paper cites.
D. C. S. Aeberhard and O. de Vel, “Comparison of classifiers in high dimensional settings,” Tech. Rep. no. 92-02 , 1992
1992
Earlier work this paper cites.
A. Aamodt and E. Plaza., “Case-based reasoning: Foundational issues, methodological variations, and systemapproaches.” AI communications , 1994
1994
Earlier work this paper cites.
O. L. M. William H. Wolberg, W. Nick Street, “Breast cancer wisconsin (diagnostic) data set,” https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic) , 1995
1995
Earlier work this paper cites.
1998
Earlier work this paper cites.
S. Boyd and L. Vandenberghe, Convex Optimization . New York, NY, USA: Cambridge University Press, 2004
2004
Earlier work this paper cites.
2006
Earlier work this paper cites.
2010
Earlier work this paper cites.
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. S. Zemel, “Fairness through awareness,” in Innovations in Theoretical Computer Science 2012, Cambridge, MA, USA, January 8-10, 2012 , S. Goldwasser, Ed. ACM, 2012, pp. 214–226. [Online]. Available: https://doi.org/10.1145/2090236.2090255
2012
Earlier work this paper cites.
J. Angwin, J. Larson, S. Mattu, and L. Kirchner, “Machine bias - there’s software used across the country to predict future criminals. and it’s biased against blacks.” 2016. [Online]. Available: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
2016
Earlier work this paper cites.
K. Waddell, “How algorithms can bring down minorities’ credit scores,” The Atlantic , 2016
2016
Cited alongside, same era.
E. parliament and council, “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),” https://eur-lex.europa.eu/eli/reg/2016/679/oj , 2016
2016
Cited alongside, same era.
M. T. Ribeiro, S. Singh, and C. Guestrin, “Model-agnostic interpretability of machine learning,” in ICML Workshop on Human Interpretability in Machine Learning (WHI) , 2016
2016
Cited alongside, same era.
M. T. Ribeiro, S. Singh, and C. Guestrin, “”why should i trust you?”: Explaining the predictions of any classifier,” in Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD ’16. New York, NY, USA: ACM, 2016, pp. 1135–1144. [Online]. Available: http://doi.acm.org/10.1145/2939672.2939778
2018
Later among the works it cites.
D. Alvarez-Melis and T. S. Jaakkola, “Towards robust interpretability with self-explaining neural networks,” in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada , S. Bengio, H. M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., 2018, pp. 7786–7795. [Online]. Available: https://proceedings.neurips.cc/paper/2018/hash/3e9f0fc9b2f89e043bc6233994dfcf76-Abstract.html
2018
Later among the works it cites.
C. Molnar, Interpretable Machine Learning , 2019, https://christophm.github.io/interpretable-ml-book/
2019
Later among the works it cites.
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2016
Cited alongside, same era.
B. Kim, O. Koyejo, and R. Khanna, “Examples are not enough, learn to criticize! criticism for interpretability,” in Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain , 2016, pp. 2280–2288
2016
Cited alongside, same era.
2017
Cited alongside, same era.
P. W. Koh and P. Liang, “Understanding black-box predictions via influence functions,” in Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 , 2017, pp. 1885–1894. [Online]. Available: http://proceedings.mlr.press/v70/koh17a.html
2017
Cited alongside, same era.
2017
Cited alongside, same era.
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal, “Explaining explanations: An overview of interpretability of machine learning,” in 5th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2018, Turin, Italy, October 1-3, 2018 , 2018, pp. 80–89. [Online]. Available: https://doi.org/10.1109/DSAA.2018.00018
2018
Cited alongside, same era.
R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi, “A survey of methods for explaining black box models,” ACM Comput. Surv. , vol. 51, no. 5, pp. 93:1–93:42, Aug. 2018. [Online]. Available: http://doi.acm.org/10.1145/3236009
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Later among the works it cites.
R. M. J. Byrne, “Counterfactuals in explainable artificial intelligence (xai): Evidence from human reasoning,” in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 . International Joint Conferences on Artificial Intelligence Organization, 7 2019, pp. 6276–6282. [Online]. Available: https://doi.org/10.24963/ijcai.2019/876
2019
Later among the works it cites.
J. Heo, S. Joo, and T. Moon, “Fooling neural network interpretations via adversarial model manipulation,” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada , H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, and R. Garnett, Eds., 2019, pp. 2921–2932. [Online]. Available: https://proceedings.neurips.cc/paper/2019/hash/7fea637fd6d02b8f0adf6f7dc36aed93-Abstract.html
2019
Later among the works it cites.
2019
Later among the works it cites.
C. J. Anders, P. Pasliev, A. Dombrowski, K. Müller, and P. Kessel, “Fairwashing explanations with off-manifold detergent,” in Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event , ser. Proceedings of Machine Learning Research, vol. 119. PMLR, 2020, pp. 314–323. [Online]. Available: http://proceedings.mlr.press/v119/anders20a.html
2020
Later among the works it cites.
S. Verma, J. Dickerson, and K. Hines, “Counterfactual explanations for machine learning: A review,” 2020
2020
Later among the works it cites.
R. Binns, “On the apparent conflict between individual and group fairness,” in FAT* ’20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020 , M. Hildebrandt, C. Castillo, E. Celis, S. Ruggieri, L. Taylor, and G. Zanfir-Fortuna, Eds. ACM, 2020, pp. 514–524. [Online]. Available: https://doi.org/10.1145/3351095.3372864
2020
Later among the works it cites.
L. Hancox-Li, “Robustness in machine learning explanations: Does it matter?” ser. FAT* ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 640–647. [Online]. Available: https://doi.org/10.1145/3351095.3372836
2020
Later among the works it cites.
A. Artelt and B. Hammer, “Convex density constraints for computing plausible counterfactual explanations,” 29th International Conference on Artificial Neural Networks (ICANN) , 2020
2020
Later among the works it cites.
V. Nanda, S. Dooley, S. Singla, S. Feizi, and J. P. Dicker-son., “Fairness through robustness:investigating robustness disparity in deep learning,” in FAccT’21: Conference on Fairness, Accountability, and Transparency, Virtual Event, Canada, March 3–10, 2021 . ACM, 2021. [Online]. Available: https://doi.org/10.1145/3442188.3445910
2021
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J. von Kügelgen, A.-H. Karimi, U. Bhatt, I. Valera, A. Weller, and B. Schölkopf, “On the fairness of causal algorithmic recourse,” 2021
2021
Closest in time.