  The causes underlying unfair decision making are complex, being internalised
in different ways by decision makers, other actors dealing with data and
models, and ultimately by the individuals being affected by these decisions.
One frequent manifestation of all these latent causes arises in the form of
missing values: protected groups are more reluctant to give information that
could be used against them, delicate information for some groups can be erased
by human operators, or data acquisition may simply be less complete and
systematic for minority groups. As a result, missing values and bias in data
are two phenomena that are tightly coupled. However, most recent techniques,
libraries and experimental results dealing with fairness in machine learning
have simply ignored missing data. In this paper, we claim that fairness
research should not miss the opportunity to deal properly with missing data. To
support this claim, (1) we analyse the sources of missing data and bias, and we
map the common causes, (2) we find that rows containing missing values are
usually fairer than the rest, which should not be treated as the uncomfortable
ugly data that different techniques and libraries get rid of at the first
occasion, and (3) we study the trade-off between performance and fairness when
the rows with missing values are used (either because the technique deals with
them directly or by imputation methods). We end the paper with a series of
recommended procedures about what to do with missing data when aiming for fair
decision making.
