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To enable an ethical and legal use of machine learning algorithms, they must both be fair and protect the privacy of those whose data are being used.
Crafting papers on machine learning
Langley, P · 2000
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Evaluation of Fairness Trade-offs in Predicting Student Success
Lee, H. and Kizilcec, R. F · 2007
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Fairness in Machine Learning: A Survey
Caton, S. and Haas, C · 2010
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Keras, 2015
Chollet, F · 2015
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Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S · 2015
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Discrimination- and privacy-aware patterns
Hajian, S., Domingo-Ferrer, J., Monreale, A., Pedreschi, D., and Giannotti, F · 2015
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UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
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Adam: A method for stochastic optimization, 2017
Kingma, D. P. and Ba, J · 2017
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Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martina, J., Mehta, S., Mojsilovic, A., Nagar, S., Ramamurthy, K. N., Richards, J., Saha, D., Sattigeri, P., Singh, M., Varshney, K. R., and Zhang, Y · 2018
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The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
Corbett-Davies, S. and Goel, S · 2018
Cited alongside, same era.
Correspondences between Privacy and Nondiscrimination: Why They Should Be Studied Together
Datta, A., Sen, S., and Tschantz, M. C · 2018
Cited alongside, same era.
Privacy for All: Ensuring Fair and Equitable Privacy Protections
Ekstrand, M. D., Joshaghani, R., and Mehrpouyan, H · 2018
Cited alongside, same era.
Differential Privacy: A Primer for a Non-Technical Audience
Wood, A., Altman, M., Bembenek, A., Bun, M., Gaboardi, M., Honaker, J., Nissim, K., O’Brien, D., Steinke, T., and Vadhan, S · 2018
Cited alongside, same era.
On the Compatibility of Privacy and Fairness
Cummings, R., Gupta, V., Kimpara, D., and Morgenstern, J · 2019
Cited alongside, same era.
Achieving differential privacy and fairness in logistic regression
Xu, D., Yuan, S., and Wu, X · 2019
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Mitigating discrimination in clinical machine learning decision support using algorithmic processing techniques
Briggs, E. and Hollmén, J · 2020
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Differentially private and fair classification via calibrated functional mechanism
Ding, J., Zhang, X., Li, X., Wang, J., Yu, R., and Pan, M · 2020
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Preserving differential privacy in deep neural networks with relevance-based adaptive noise imposition
Gong, M., Pan, K., Xie, Y., Qin, A., and Tang, Z · 2020
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Bias mitigation with aif360: A comparative study
Hufthammer, K. T., Aasheim, T. H., Ånneland, S., Brynjulfsen, H., and Slavkovik, M · 2020
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A comparative study of fairness-enhancing interventions in machine learning
Friedler, S. A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E. P., and Roth, D · 2019
Cited alongside, same era.
Differentially Private Fair Learning
Jagielski, M., Kearns, M., Mao, J., Oprea, A., Roth, A., Sharifi-Malvajerdi, S., and Ullman, J · 2019
Cited alongside, same era.
Evaluating differentially private machine learning in practice
Jayaraman, B. and Evans, D · 2019
Cited alongside, same era.
A General Approach to Adding Differential Privacy to Iterative Training Procedures
McMahan, H. B., Andrew, G., Erlingsson, U., Chien, S., Mironov, I., Papernot, N., and Kairouz, P · 2019
Cited alongside, same era.
Yeom, S., Giacomelli, I., Menaged, A., Fredrikson, M., and Jha, S · 2020
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Not one but many Tradeoffs: Privacy Vs. Utility in Differentially Private Machine Learning
Zhao, B. Z. H., Kaafar, M. A., and Kourtellis, N · 2020
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More than privacy: applying differential privacy in key areas of artificial intelligence
Zhu, T., Ye, D., Wang, W., Zhou, W., and Yu, P. S · 2020
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