A snapshot of the frontiers of fairness in machine learning

[1] Agarwal A., Beygelzimer A., Dudik M., Langford J., Wallach H., A reductions approach to fair classification, Proceedings of the 35th Intern. Conf. Machine Learning. ICML, Jmlr Workshop and Conference Proceedings, pp. 2569-2577, (2018)

[2] Bastani H., Bayati M., Khosravi K., Exploiting the Natural Exploration in Contextual Bandits., (2017)

[3] Becker G.S., The Economics of Discrimination., (2010)

[4] Berk R., Heidari H., Jabbari S., Kearns M., Roth A., Fairness in criminal justice risk assessments: The state of the art, Sociological Methods & Research, (2018)

[5] Beutel A., Chen J., Zhao Z., Chi E.H., Data Decisions and Theoretical Implications When Adversarially Learning Fair Representations., (2017)

[6] Bird S., Barocas S., Crawford K., Diaz F., Wallach H., Exploring or exploiting? Social and ethical implications of autonomous experimentation in AI, Proceedings of Workshop on Fairness, Accountability, and Transparency in Machine Learning., (2016)

[7] Bolukbasi T., Chang K.-W., Zou J.Y., Saligrama V., Kalai A.T., Man is to computer programmer as woman is to homemaker? Debiasing word embeddings, Advances in Neural Information Processing Systems, pp. 4349-4357, (2016)

[8] Bower A., Et al., Fair Pipelines., (2017)

[9] Buolamwini J., Gebru T., Gender shades: Intersectional accuracy disparities in commercial gender classification, Proceedings of the Conference on Fairness, Accountability and Transparency., pp. 77-91, (2018)

[10] Calders T., Verwer S., Three naive Bayes approaches for discrimination-free classification, Data Mining and Knowledge Discovery, 21, 2, pp. 277-292, (2010)

[1] Agarwal A., Beygelzimer A., Dudik M., Langford J., Wallach H., A reductions approach to fair classification, Proceedings of the 35th Intern. Conf. Machine Learning. ICML, Jmlr Workshop and Conference Proceedings, pp. 2569-2577, (2018)

[2] Bastani H., Bayati M., Khosravi K., Exploiting the Natural Exploration in Contextual Bandits., (2017)

[3] Becker G.S., The Economics of Discrimination., (2010)

[4] Berk R., Heidari H., Jabbari S., Kearns M., Roth A., Fairness in criminal justice risk assessments: The state of the art, Sociological Methods & Research, (2018)

[5] Beutel A., Chen J., Zhao Z., Chi E.H., Data Decisions and Theoretical Implications When Adversarially Learning Fair Representations., (2017)

[6] Bird S., Barocas S., Crawford K., Diaz F., Wallach H., Exploring or exploiting? Social and ethical implications of autonomous experimentation in AI, Proceedings of Workshop on Fairness, Accountability, and Transparency in Machine Learning., (2016)

[7] Bolukbasi T., Chang K.-W., Zou J.Y., Saligrama V., Kalai A.T., Man is to computer programmer as woman is to homemaker? Debiasing word embeddings, Advances in Neural Information Processing Systems, pp. 4349-4357, (2016)

[8] Bower A., Et al., Fair Pipelines., (2017)

[9] Buolamwini J., Gebru T., Gender shades: Intersectional accuracy disparities in commercial gender classification, Proceedings of the Conference on Fairness, Accountability and Transparency., pp. 77-91, (2018)

[10] Calders T., Verwer S., Three naive Bayes approaches for discrimination-free classification, Data Mining and Knowledge Discovery, 21, 2, pp. 277-292, (2010)