Built on

Azzalini, A., Valle, A.D.: The multivariate skew-normal distribution. Biometrika 83
1996
Earlier work this paper cites.
Cohen, S.B.: The medical expenditure panel survey: an overview. Effective Clinical Practice 5
2002
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Azzalini, A.: The skew-normal distribution and related multivariate families. Scandinavian Journal of Statistics 32
2005
Earlier work this paper cites.
Steel, E., Angwin, J.: On the web’s cutting edge, anonymity in name only. The Wall Street Journal 4
2010
Earlier work this paper cites.
Zimdars, A.: Fairness and undergraduate admission: a qualitative exploration of admissions choices at the university of oxford. Oxford Review of Education 36
2010
Earlier work this paper cites.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference. pp. 214–226 (2012)
2012
Earlier work this paper cites.
Sweeney, L.: Discrimination in online ad delivery. Communications of the ACM 56
2013
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Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision. pp. 3730–3738 (2015)
2015
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Angwin, J., Larson, J., Mattu, S., Kirchner, L.: There’s software used across the country to predict future criminals. ProPublica (2016)
2016
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Goel, S., Rao, J.M., Shroff, R., et al.: Precinct or prejudice? understanding racial disparities in new york city’s stop-and-frisk policy. Annals of Applied Statistics 10
2016
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Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Bechavod, Y., Ligett, K.: Penalizing unfairness in binary classification. arXiv preprint arXiv:1707.00044 (2017)
Original
2017
Earlier work this paper cites.
Dua, D., Graff, C.: UCI machine learning repository. (2007) (2017), http://archive.ics.uci.edu/ml
2017
Earlier work this paper cites.
Dua, D., Graff, C.: UCI machine learning repository. (2017) (2017), http://archive.ics.uci.edu/ml
2017
Earlier work this paper cites.
Kusner, M.J., Loftus, J.R., Russell, C., Silva, R.: Counterfactual fairness. arXiv preprint arXiv:1703.06856 (2017)
Original
2017
Earlier work this paper cites.

Similar

Bellamy, R.K., Dey, K., Hind, M., Hoffman, S.C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., et al.: Ai fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943 (2018)
Original
2018
Cited alongside, same era.
Chen, I., Johansson, F.D., Sontag, D.: Why is my classifier discriminatory? arXiv preprint arXiv:1805.12002 (2018)
Original
2018
Cited alongside, same era.
Hashimoto, T., Srivastava, M., Namkoong, H., Liang, P.: Fairness without demographics in repeated loss minimization. In: International Conference on Machine Learning. pp. 1929–1938. PMLR (2018)
2018
Cited alongside, same era.
Kleinberg, J., Ludwig, J., Mullainathan, S., Rambachan, A.: Algorithmic fairness. In: Aea papers and proceedings. vol. 108, pp. 22–27 (2018)
2018
Cited alongside, same era.

Then

Du, M., Yang, F., Zou, N., Hu, X.: Fairness in deep learning: A computational perspective. IEEE Intelligent Systems (2020)
2020
Later among the works it cites.
Lahoti, P., Beutel, A., Chen, J., Lee, K., Prost, F., Thain, N., Wang, X., Chi, E.H.: Fairness without demographics through adversarially reweighted learning. arXiv preprint arXiv:2006.13114 (2020)
Original
2020
Later among the works it cites.
Nam, J., Cha, H., Ahn, S., Lee, J., Shin, J.: Learning from failure: Training debiased classifier from biased classifier. arXiv preprint arXiv:2007.02561 (2020)
Original
2020
Later among the works it cites.
R., Y., B., S., C., E.J.: Achieving equalized odds by resampling sensitive attributes. arXiv preprint arXiv:2006.04292 (2020)
Original
2020
Later among the works it cites.

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2022

Mitigating Algorithmic Bias with Limited Annotations

Wang, Guanchu, Du, Mengnan, Liu, Ninghao et al.

Understand

Existing work on fairness modeling commonly assumes that sensitive attributes for all instances are fully available, which may not be true in many real-world applications due to the high cost of acquiring sensitive information.

When sensitive attributes are not disclosed or available, it is needed to manually annotate a small part of the training data to mitigate bias.
However, the skewed distribution across different sensitive groups preserves the skewness of the original dataset in the annotated subset, which leads to non-optimal bias mitigation.
To tackle this challenge, we propose Active Penalization Of Discrimination (APOD), an interactive framework to guide the limited annotations towards maximally eliminating the effect of algorithmic bias.

Built on

Azzalini, A., Valle, A.D.: The multivariate skew-normal distribution. Biometrika 83
1996
Earlier work this paper cites.
Cohen, S.B.: The medical expenditure panel survey: an overview. Effective Clinical Practice 5
2002
Earlier work this paper cites.
Azzalini, A.: The skew-normal distribution and related multivariate families. Scandinavian Journal of Statistics 32
2005
Earlier work this paper cites.
Steel, E., Angwin, J.: On the web’s cutting edge, anonymity in name only. The Wall Street Journal 4
2010
Earlier work this paper cites.
Zimdars, A.: Fairness and undergraduate admission: a qualitative exploration of admissions choices at the university of oxford. Oxford Review of Education 36
2010
Earlier work this paper cites.
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference. pp. 214–226 (2012)
2012
Earlier work this paper cites.
Sweeney, L.: Discrimination in online ad delivery. Communications of the ACM 56
2013
Earlier work this paper cites.
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision. pp. 3730–3738 (2015)
2015
Earlier work this paper cites.
Angwin, J., Larson, J., Mattu, S., Kirchner, L.: There’s software used across the country to predict future criminals. ProPublica (2016)
2016
Earlier work this paper cites.
Goel, S., Rao, J.M., Shroff, R., et al.: Precinct or prejudice? understanding racial disparities in new york city’s stop-and-frisk policy. Annals of Applied Statistics 10
2016
Earlier work this paper cites.
Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Bechavod, Y., Ligett, K.: Penalizing unfairness in binary classification. arXiv preprint arXiv:1707.00044 (2017)
Original
2017
Earlier work this paper cites.
Dua, D., Graff, C.: UCI machine learning repository. (2007) (2017), http://archive.ics.uci.edu/ml
2017
Earlier work this paper cites.
Dua, D., Graff, C.: UCI machine learning repository. (2017) (2017), http://archive.ics.uci.edu/ml
2017
Earlier work this paper cites.
Kusner, M.J., Loftus, J.R., Russell, C., Silva, R.: Counterfactual fairness. arXiv preprint arXiv:1703.06856 (2017)
Original
2017
Earlier work this paper cites.

Similar

Bellamy, R.K., Dey, K., Hind, M., Hoffman, S.C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., et al.: Ai fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943 (2018)
Original
2018
Cited alongside, same era.
Chen, I., Johansson, F.D., Sontag, D.: Why is my classifier discriminatory? arXiv preprint arXiv:1805.12002 (2018)
Original
2018
Cited alongside, same era.
Hashimoto, T., Srivastava, M., Namkoong, H., Liang, P.: Fairness without demographics in repeated loss minimization. In: International Conference on Machine Learning. pp. 1929–1938. PMLR (2018)
2018
Cited alongside, same era.
Kleinberg, J., Ludwig, J., Mullainathan, S., Rambachan, A.: Algorithmic fairness. In: Aea papers and proceedings. vol. 108, pp. 22–27 (2018)
2018
Cited alongside, same era.

Then

Du, M., Yang, F., Zou, N., Hu, X.: Fairness in deep learning: A computational perspective. IEEE Intelligent Systems (2020)
2020
Later among the works it cites.
Lahoti, P., Beutel, A., Chen, J., Lee, K., Prost, F., Thain, N., Wang, X., Chi, E.H.: Fairness without demographics through adversarially reweighted learning. arXiv preprint arXiv:2006.13114 (2020)
Original
2020
Later among the works it cites.
Nam, J., Cha, H., Ahn, S., Lee, J., Shin, J.: Learning from failure: Training debiased classifier from biased classifier. arXiv preprint arXiv:2007.02561 (2020)
Original
2020
Later among the works it cites.
R., Y., B., S., C., E.J.: Achieving equalized odds by resampling sensitive attributes. arXiv preprint arXiv:2006.04292 (2020)
Original
2020
Later among the works it cites.

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Rajkomar, A., Hardt, M., Howell, M.D., Corrado, G., Chin, M.H.: Ensuring fairness in machine learning to advance health equity. Annals of internal medicine 169

2018

Cited alongside, same era.

Sener, O., Savarese, S.: Active learning for convolutional neural networks: A core-set approach (2018)

2018

Cited alongside, same era.

Verma, S., Rubin, J.: Fairness definitions explained. In: 2018 ieee/acm international workshop on software fairness (fairware). pp. 1–7. IEEE (2018)

2018

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Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. pp. 335–340 (2018)

2018

Cited alongside, same era.

Creager, E., Madras, D., Jacobsen, J.H., Weis, M., Swersky, K., Pitassi, T., Zemel, R.: Flexibly fair representation learning by disentanglement. In: International Conference on Machine Learning. pp. 1436–1445. PMLR (2019)

2019

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Li, Y., Vasconcelos, N.: Repair: Removing representation bias by dataset resampling. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9572–9581 (2019)

2019

Cited alongside, same era.

Sagawa, S., Koh, P.W., Hashimoto, T.B., Liang, P.: Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. arXiv preprint arXiv:1911.08731 (2019)

Original

2019

Cited alongside, same era.

Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.W., Wang, W.Y.: Mitigating gender bias in natural language processing: Literature review. arXiv preprint arXiv:1906.08976 (2019)

Original

2019

Cited alongside, same era.

Slack, D., Friedler, S.A., Givental, E.: Fairness warnings and fair-maml: learning fairly with minimal data. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. pp. 200–209 (2020)

2020