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As machine learning algorithms are increasingly deployed for high-impact automated decision making, ethical and increasingly also legal standards demand that they treat all individuals fairly, without discrimination based on their age, gender, race or other sensitive traits.
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Woodworth, B., Gunasekar, S., Ohannessian, M. I., and Srebro, N · 2017
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Zafar, M. B., Valera, I., Rogriguez, M. G., and Gummadi, K. P · 2017
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A survey on network embedding
Cui, P., Wang, X., Pei, J., and Zhu, W · 2018
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Algorithmic glass ceiling in social networks: The effects of social recommendations on network diversity
Stoica, A.-A., Riederer, C., and Chaintreau, A · 2018
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Bose, A. and Hamilton, W · 2019
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Creager, E., Madras, D., Jacobsen, J.-H., Weis, M., Swersky, K., Pitassi, T., and Zemel, R · 2019
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Ekstrand, M. D., Tian, M., Kazi, M. R. I., Mehrpouyan, H., and Kluver, D · 2018
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Madras, D., Creager, E., Pitassi, T., and Zemel, R · 2018
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Palowitch, J. and Perozzi, B · 2019
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Fairwalk: Towards fair graph embedding
Rahman, T., Surma, B., Backes, M., and Zhang, Y · 2019
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