Recent state-of-the-art recommender systems predominantly rely on either implicit or explicit feedback from users to suggest new items.While effective in recommending novel options, many recommender systems often use uninterpretable embeddings to represent user preferences.This lack of transparency not only limits user understanding of why certain items are suggested but also reduces the user's ability to scrutinize and modify their preferences, thereby affecting their ability to receive a list of preferred recommendations.Given the recent advances in Large Language Models (LLMs), we investigate how a properly crafted prompt can be used to summarize a user's preferences from past reviews and recommend items based only on language-based preferences.In particular, we study how LLMs can be prompted to generate a natural language (NL) user profile that holistically describe a user's preferences.These NL profiles can then be leveraged to fine-tune a LLM using only NL profiles to make transparent and scrutable recommendations.Furthermore, we validate the scrutability of our user profile-based recommender by investigating the impact on recommendation changes after editing NL user profiles.According to our evaluations of the model's rating prediction performance on two benchmarking rating prediction datasets, we observe that this novel approach maintains a performance level on par with established recommender systems in a warm-start setting.With a systematic analysis into the effect of updating user profiles and system prompts, we show the advantage of our approach in easier adjustment of user preferences and a greater autonomy over users' received recommendations.

Conversational Machine Reading (CMR) systems answer high-level user questions by interpreting contextual information, asking clarification questions, and generating human-like responses. While effective, such systems often use knowledge about the task and the user in a non-transparent and non-scrutable way. For example, if a user wants to ask questions like “Why are you asking this?” or “Why is this the correct answer?”, the system should be able to highlight and return the relevant information that led to the decision in an interpretable manner. Similarly, if a user scrutinizes and edits their user profile, the final output of the model should change accordingly. To test the transparency and scrutability of conversational machine reading systems, we formalize two new tasks by extending the ShARC dataset to create the EXtrA-ShARC dataset. For transparency, we propose a baseline model that can simultaneously extract explanations and answer the user’s question. We will also publicly release counterfactual user profiles to test scrutability for all CMR models. Our dataset opens up a range of research directions for using natural language explanations and counterfactual profiles in conversational systems, both for evaluating the model and increasing transparency for end users.

Search engines often provide only limited explanation on why results are ranked in a particular order. This lack of transparency prevents users from understanding results and can potentially give rise to biased or unfair systems. Opaque search engines may also hurt user trust in the presented ranking. This paper presents an investigation of system quality when different degrees of explanation are provided on search engine result pages. Our user study demonstrates that the inclusion of even simplistic explanations leads to better transparency, increased user trust and better search efficiency.