  While implicit feedback (e.g., clicks, dwell times, etc.) is an abundant and
attractive source of data for learning to rank, it can produce unfair ranking
policies for both exogenous and endogenous reasons. Exogenous reasons typically
manifest themselves as biases in the training data, which then get reflected in
the learned ranking policy and often lead to rich-get-richer dynamics.
Moreover, even after the correction of such biases, reasons endogenous to the
design of the learning algorithm can still lead to ranking policies that do not
allocate exposure among items in a fair way. To address both exogenous and
endogenous sources of unfairness, we present the first learning-to-rank
approach that addresses both presentation bias and merit-based fairness of
exposure simultaneously. Specifically, we define a class of amortized
fairness-of-exposure constraints that can be chosen based on the needs of an
application, and we show how these fairness criteria can be enforced despite
the selection biases in implicit feedback data. The key result is an efficient
and flexible policy-gradient algorithm, called FULTR, which is the first to
enable the use of counterfactual estimators for both utility estimation and
fairness constraints. Beyond the theoretical justification of the framework, we
show empirically that the proposed algorithm can learn accurate and fair
ranking policies from biased and noisy feedback.
