  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 summaries for massive datasets? To this
end, we develop the first streaming approximation algorithms for submodular
maximization under fairness constraints, for both monotone and non-monotone
functions. We validate our findings empirically on exemplar-based clustering,
movie recommendation, DPP-based summarization, and maximum coverage in social
networks, showing that fairness constraints do not significantly impact
utility.
