Predictive models are omnipresent in automated and assisted decision making scenarios. But for the most part they are used as black boxes which output a prediction without understanding partially or even completely how di erent features in uence the model prediction avoiding algorithmic transparency. Rankings are ordering over items encoding implicit comparisons typically learned using a family of features using learning-to-rank models. In this paper we focus on how best we can understand the decisions made by a ranker in a post-hoc model agnostic manner. We operate on the notion of interpretability based on explainability of rankings over an interpretable feature space. Furthermore we train a tree based model (inherently interpretable) using labels from the ranker, called secondary training data to provide explanations. Consequently, we a empt to study how well does a subset of features, potentially interpretable, explain the full model under di erent training sizes and algorithms. We do experiments on the learning to rank datasets with 30k queries and report results that serve show in certain settings we can learn a faithful interpretable ranker.
Closest to our work is the recent work by Singh and Anand REF which tried to approximate an already trained learning to rank model by a subset of (the original features) interpretable features using secondary training data from the output of the original model.
Posthoc Interpretability of Learning to Rank Models using Secondary Training Data
ArXiv
ArXiv
Computer Science