21 research outputs found
Fair Inputs and Fair Outputs: The Incompatibility of Fairness in Privacy and Accuracy
Fairness concerns about algorithmic decision-making systems have been mainly
focused on the outputs (e.g., the accuracy of a classifier across individuals
or groups). However, one may additionally be concerned with fairness in the
inputs. In this paper, we propose and formulate two properties regarding the
inputs of (features used by) a classifier. In particular, we claim that fair
privacy (whether individuals are all asked to reveal the same information) and
need-to-know (whether users are only asked for the minimal information required
for the task at hand) are desirable properties of a decision system. We explore
the interaction between these properties and fairness in the outputs (fair
prediction accuracy). We show that for an optimal classifier these three
properties are in general incompatible, and we explain what common properties
of data make them incompatible. Finally we provide an algorithm to verify if
the trade-off between the three properties exists in a given dataset, and use
the algorithm to show that this trade-off is common in real data