Missing the missing values: The ugly duckling of fairness in machine learning

Nowadays, there is an increasing concern in machine learning about the causes underlying unfair decision making, that is, algorithmic decisions discriminating some groups over others, especially with groups that are defined over protected attributes, such as gender, race and nationality. Missing values are one frequent manifestation of all these latent causes: protected groups are more reluctant to give information that could be used against them, sensitive information for some groups can be erased by human operators, or data acquisition may simply be less complete and systematic for minority groups. However, most recent techniques, libraries and experimental results dealing with fairness in machine learning have simply ignored missing data. In this paper, we present the first comprehensive analysis of the relation between missing values and algorithmic fairness for machine learning: (1) we analyse the sources of missing data and bias, mapping the common causes, (2) we find that rows containing missing values are usually fairer than the rest, which should discourage the consideration of missing values as the uncomfortable ugly data that different techniques and libraries for handling algorithmic bias get rid of at the first occasion, (3) we study