  Understanding why a model makes certain predictions is crucial when adapting
it for real world decision making. LIME is a popular model-agnostic feature
attribution method for the tasks of classification and regression. However, the
task of learning to rank in information retrieval is more complex in comparison
with either classification or regression. In this work, we extend LIME to
propose Rank-LIME, a model-agnostic, local, post-hoc linear feature attribution
method for the task of learning to rank that generates explanations for ranked
lists.
  We employ novel correlation-based perturbations, differentiable ranking loss
functions and introduce new metrics to evaluate ranking based additive feature
attribution models. We compare Rank-LIME with a variety of competing systems,
with models trained on the MS MARCO datasets and observe that Rank-LIME
outperforms existing explanation algorithms in terms of Model Fidelity and
Explain-NDCG. With this we propose one of the first algorithms to generate
additive feature attributions for explaining ranked lists.
