Fairness and Missing Values

Understand

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.