Fetching the paper…
Reading the bibliography…
Deep learning is increasingly being used in high-stake decision making applications that affect individual lives.
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, “Fairness through awareness,” in Proceedings of the 3rd innovations in theoretical computer science conference , 2012
2012
Earlier work this paper cites.
F. Kamiran and T. Calders, “Data preprocessing techniques for classification without discrimination,” Knowledge and Information Systems (KAIS) , 2012
2012
Earlier work this paper cites.
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma, “Fairness-aware classifier with prejudice remover regularizer,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2012
2012
Earlier work this paper cites.
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian, “Certifying and removing disparate impact,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) . ACM, 2015
2015
Earlier work this paper cites.
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” ICLR workshop , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
T. Bolukbasi, K.-W. Chang, J. Y. Zou, V. Saligrama, and A. T. Kalai, “Man is to computer programmer as woman is to homemaker? debiasing word embeddings,” in Thirtieth Conference on Neural Information Processing Systems (NIPS) , 2016
2016
Earlier work this paper cites.
S. L. Blodgett, L. Green, and B. O’Connor, “Demographic dialectal variation in social media: A case study of african-american english,” 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2016
2016
Earlier work this paper cites.
M. Hardt, E. Price, N. Srebro et al. , “Equality of opportunity in supervised learning,” in Advances in neural information processing systems (NIPS) , 2016
2016
Earlier work this paper cites.
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep features for discriminative localization,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Earlier work this paper cites.
S. Escalera, M. Torres Torres, B. Martinez, X. Baró, H. Jair Escalante, I. Guyon, G. Tzimiropoulos, C. Corneou, M. Oliu, M. Ali Bagheri et al. , “Chalearn looking at people and faces of the world: Face analysis workshop and challenge 2016,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2016
2016
Earlier work this paper cites.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” The Journal of Machine Learning Research (JMLR) , 2016
2016
Earlier work this paper cites.
J. Zhao, T. Wang, M. Yatskar, V. Ordonez, and K.-W. Chang, “Men also like shopping: Reducing gender bias amplification using corpus-level constraints,” 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2017
2017
Earlier work this paper cites.
A. Beutel, J. Chen, Z. Zhao, and E. H. Chi, “Data decisions and theoretical implications when adversarially learning fair representations,” Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) , 2017
2017
Earlier work this paper cites.
D. Jurgens, Y. Tsvetkov, and D. Jurafsky, “Incorporating dialectal variability for socially equitable language identification,” in The 55th Annual Meeting of the Association for Computational Linguistics (ACL) , 2017
2017
Earlier work this paper cites.
A. S. Ross, M. C. Hughes, and F. Doshi-Velez, “Right for the right reasons: Training differentiable models by constraining their explanations,” Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI) , 2017
2017
Earlier work this paper cites.
F. Calmon, D. Wei, B. Vinzamuri, K. N. Ramamurthy, and K. R. Varshney, “Optimized pre-processing for discrimination prevention,” in Advances in Neural Information Processing Systems (NIPS) , 2017, pp. 3992–4001
2017
Earlier work this paper cites.
Z. Zhang, Y. Song, and H. Qi, “Age progression/regression by conditional adversarial autoencoder,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
Cited alongside, same era.
P. Gajane and M. Pechenizkiy, “On formalizing fairness in prediction with machine learning,” Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) , 2018
2018
Cited alongside, same era.
S. Kiritchenko and S. M. Mohammad, “Examining gender and race bias in two hundred sentiment analysis systems,” Proceedings of the 7th Joint Conference on Lexical and Computational Semantics , 2018
2018
Cited alongside, same era.
J. Buolamwini and T. Gebru, “Gender shades: Intersectional accuracy disparities in commercial gender classification,” in Conference on Fairness, Accountability and Transparency (FAT*) , 2018, pp. 77–91
2018
Cited alongside, same era.
2019
Closest in time.
I. Y. Chen, P. Szolovits, and M. Ghassemi, “Can ai help reduce disparities in general medical and mental health care?” AMA journal of ethics , 2019
2019
Closest in time.
2019
Closest in time.
A. Beutel, J. Chen, T. Doshi, H. Qian, A. Woodruff, C. Luu, P. Kreitmann, J. Bischof, and E. H. Chi, “Putting fairness principles into practice: Challenges, metrics, and improvements,” AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) , 2019
2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Gururangan, S. Swayamdipta, O. Levy, R. Schwartz, S. R. Bowman, and N. A. Smith, “Annotation artifacts in natural language inference data,” North American Chapter of the Association for Computational Linguistics (NAACL) , 2018
2018
Cited alongside, same era.
M. Du, N. Liu, Q. Song, and X. Hu, “Towards explanation of dnn-based prediction with guided feature inversion,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) , 2018
2018
Cited alongside, same era.
B. Kim, M. Wattenberg, J. Gilmer, C. Cai, J. Wexler, F. Viegas, and R. Sayres, “Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav),” International Conference on Machine Learning (ICML) , 2018
2018
Cited alongside, same era.
B. Zhou, Y. Sun, D. Bau, and A. Torralba, “Interpretable basis decomposition for visual explanation,” in European Conference on Computer Vision (ECCV) , 2018
2018
Cited alongside, same era.
R. Fong and A. Vedaldi, “Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
2018
Cited alongside, same era.
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. Wallach, “A reductions approach to fair classification,” International Conference on Machine Learning (ICML) , 2018
2018
Cited alongside, same era.
Y. Elazar and Y. Goldberg, “Adversarial removal of demographic attributes from text data,” 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) , 2018
2018
Cited alongside, same era.
D. Madras, E. Creager, T. Pitassi, and R. Zemel, “Learning adversarially fair and transferable representations,” International Conference on Machine Learning (ICML) , 2018
2018
Cited alongside, same era.
M. Du, N. Liu, F. Yang, and X. Hu, “On attribution of recurrent neural network predictions via additive decomposition,” The Web Conference (WWW) , 2019
2019
Closest in time.
2019
Closest in time.
F. Liu and B. Avci, “Incorporating priors with feature attribution on text classification,” 57th Annual Meeting of the Association for Computational Linguistics (ACL) , 2019
2019
Closest in time.
N. Quadrianto, V. Sharmanska, and O. Thomas, “Discovering fair representations in the data domain,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Closest in time.
2019
Closest in time.
M. Du, N. Liu, F. Yang, and X. Hu, “Learning credible deep neural networks with rationale regularization,” in IEEE International Conference on Data Mining (ICDM) , 2019
2019
Closest in time.
A. J. Bose and W. Hamilton, “Compositional fairness constraints for graph embeddings,” International Conference on Machine Learning (ICML) , 2019
2019
Closest in time.
E. Creager, D. Madras, J.-H. Jacobsen, M. A. Weis, K. Swersky, T. Pitassi, and R. Zemel, “Flexibly fair representation learning by disentanglement,” International Conference on Machine Learning (ICML) , 2019
2019
Closest in time.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) , 2019
2019
Closest in time.
2019
Closest in time.
M. Du, N. Liu, and X. Hu, “Techniques for interpretable machine learning,” Communications of the ACM (CACM) , 2020
2020
Closest in time.
S. Sharma, J. Henderson, and J. Ghosh, “Certifai: A common framework to provide explanations and analyse the fairness and robustness of black-box models,” in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES) , 2020
2020
Closest in time.