Pseudo-Relevance Feedback (PRF) aims to improve performance, but it can suffer from query drift, creating asymmetric impacts across queries. Current Selective PRF (sPRF) strategies attempt to mitigate this by predicting when PRF will help, but they rely on offline metrics that may not reflect actual user preferences. To bridge this gap, we propose a participatory auditing strategy that evaluates PRF's real-world impact through natural user interactions. Specifically, we design a 3×3 grid interface using Latin square ordering to mitigate position bias, and deploy Team Draft Interleaving (TDI) to collect implicit user preferences without disrupting normal search behaviour. We conduct two user studies: in our first user study, we find that only 20.9% of queries benefit from PRF, while 25.6% result in a degraded user experience. Our second study demonstrates that avoiding harmful PRF yields a 68.5% relative improvement, nearly twice the 37.0% gained from amplifying beneficial PRF, highlighting that harm avoidance is more valuable than benefit amplification. Our findings highlight the necessity for reliable sPRF and suggest that future sPRF research could benefit from incorporating interaction-aware participatory auditing when developing sPRF approaches.

Learning to Rank (LTR) models learn from historical user interactions, such as user clicks. However, there is an inherent bias in the clicks of users due to position bias, i.e., users are more likely to click highly-ranked documents than low-ranked documents. To address this bias when training LTR models, many approaches from the literature re-weight the users' click data using Inverse Propensity Scoring (IPS). IPS re-weights the user's clicks proportionately to the position in the historical ranking that a document was placed when it was clicked since low-ranked documents are less likely to be seen by a user. In this paper, we argue that low-ranked documents that are similar to highly-ranked relevant documents are also likely to be relevant. Moreover, accounting for the similarity of low-ranked documents to highly ranked relevant documents when calculating IPS can more effectively mitigate the effects of position bias. Therefore, we propose an extension to IPS, called IPSsim, that takes into consideration the similarity of documents when estimating IPS. We evaluate our IPSsim estimator using two large publicly available LTR datasets under a number of simulated user click settings, and with different numbers of training clicks. Our experiments show that our IPSsim estimator is more effective than the existing IPS estimators for learning an unbiased LTR model, particularly in top-n settings when n >= 30. For example, when n = 50, our IPSsim estimator achieves a statistically significant ~3% improvement (p < 0.05) in terms of NDCG compared to the Doubly Robust estimator from the literature.