Mark Sanderson

\begin{abstract}For information access systems, it is not enough for outputs or answers to be relevant or correct; they must also be \emph{permitted}. This paper highlights a research gap: non-disclosure obligations often concern \emph{propositions} that must not be stated, while deployed Retriev...

We investigate between-user drivers of query variability through a controlled between-subject, full-factorial user study that manipulates age, gender, and language proficiency across six backstory-driven search tasks. From initial queries, session logs, and post-task interviews, we quantify how d...

We conduct a lab eye-tracking study to examine how users interact with search engines that place Generative Artificial Intelligence (GenAI) results above traditionally ranked search results, also known as "ten blue links", and we use an engagement scale to evaluate their experience. Our aim is to...

Large language models (LLMs) are widely used for question answering (QA) but can generate biased or stereotype-driven answers due to demographic associations learned during pre-training. Existing mitigation strategies often rely on model access or fine-tuning, which limits their applicability to...

Information retrieval has long relied on evaluations that measure system performance. Improvements on standard evaluation protocols are interpreted as progress in system effectiveness, on the understanding that improved metrics indicate a better experience. However, most evaluations are a drastic...