论文信息 - Investigating Oversampling Techniques for Fair Machine Learning Models - 字舞流文

Investigating Oversampling Techniques for Fair Machine Learning Models

Boris Delibasic | Sandro Radovanovic | Sanja Rancic | Boris Delibasic | S. Radovanović | Sanja Rancic

[1] Hany Farid,et al. The accuracy, fairness, and limits of predicting recidivism , 2018, Science Advances.

[2] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[3] Niladri B. Syam,et al. Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice , 2018 .

[4] Toon Calders,et al. Data preprocessing techniques for classification without discrimination , 2011, Knowledge and Information Systems.

[5] Kadan Aljoumaa,et al. Customer churn prediction in telecom using machine learning in big data platform , 2019, Journal of Big Data.

[6] Avi Feller,et al. Algorithmic Decision Making and the Cost of Fairness , 2017, KDD.

[7] Erez Shmueli,et al. Algorithmic Fairness , 2020, ArXiv.

[8] Kamil Zbikowski,et al. Direct marketing campaigns in retail banking with the use of deep learning and random forests , 2019, Expert Syst. Appl..

[9] Michael Carl Tschantz,et al. Automated Experiments on Ad Privacy Settings , 2014, Proc. Priv. Enhancing Technol..

[10] Yehezkel S. Resheff,et al. Paired-Consistency: An Example-Based Model-Agnostic Approach to Fairness Regularization in Machine Learning , 2019, PKDD/ECML Workshops.

[11] Madeleine Udell,et al. Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved , 2018, FAT.