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Fairness in Streaming Submodular Maximization: Algorithms and Hardness
[article]
2020
arXiv
pre-print
Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data. However, if datapoints have sensitive attributes such as gender or age, such machine learning algorithms, left unchecked, are known to exhibit bias: under- or over-representation of particular groups. This has made the design of fair machine learning algorithms increasingly important. In this work we address the question: Is it possible to create fair
arXiv:2010.07431v2
fatcat:tzmdn4rnljcepa3nuew7m476tq