  Search systems control the exposure of ranked content to searchers. In many
cases, creators value not only the exposure of their content but, moreover, an
understanding of the specific searches where the content is surfaced. The
problem of identifying which queries expose a given piece of content in the
ranking results is an important and relatively under-explored search
transparency challenge. Exposing queries are useful for quantifying various
issues of search bias, privacy, data protection, security, and search engine
optimization.
  Exact identification of exposing queries in a given system is computationally
expensive, especially in dynamic contexts such as web search. We explore the
feasibility of approximate exposing query identification (EQI) as a retrieval
task by reversing the role of queries and documents in two classes of search
systems: dense dual-encoder models and traditional BM25 models. We then propose
how this approach can be improved through metric learning over the retrieval
embedding space. We further derive an evaluation metric to measure the quality
of a ranking of exposing queries, as well as conducting an empirical analysis
focusing on various practical aspects of approximate EQI. Overall, our work
contributes a novel conception of transparency in search systems and
computational means of achieving it.
