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2017

Fair Pipelines

Bower, Amanda, Kitchen, Sarah N., Niss, Laura et al.

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This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions.

In particular, this work studies how fairness propagates through a compound decision-making processes, which we call a pipeline.
Prior work in algorithmic fairness only focuses on fairness with respect to one decision.
However, many decision-making processes require more than one decision.

Fair Pipelines · Around

Built on

Fairness Through Awareness
Original
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel. 2011 · 2011
Earlier work this paper cites.
Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems. In 2016 IEEE Symposium on Security and Privacy (SP)
A. Datta, S. Sen, and Y. Zick. 2016 · 2016
Earlier work this paper cites.

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