Speakers: Professor Iadh Ounis & Dr Graham McDonald, University of Glasgow
Abstract: Organisations such as governments, archives, and cultural heritage institutions are typically required to provide public access to large document collections in the interest of transparency, accountability, and research. However, such collections can often contain sensitive information such as personal data, confidential communications, or security-relevant information that cannot be safely released to the public. Traditional manual sensitivity review processes for removing sensitive documents prior to public release are expensive, time consuming, and can be difficult to scale. Therefore, there is a need for technical solutions that support safe and responsible search over such collections while also protecting any sensitive information, so that such information is not leaked into the search results. In this talk, we will present our recent work on developing AI‑powered technologies to help organisations safely release large collections of documents to the public without the need for a costly manual sensitivity review. In particular, our talk will primarily focus on our approaches to sensitivity‑aware search, which apply sensitivity‑filtering interventions at each stage of a modern search pipeline. Rather than treating sensitivity detection as a single classification problem, our work explores how safeguards can be embedded within the ranking models that are deployed throughout the search process. These safeguards reduce the risk of exposing sensitive information while still allowing the public to search and use the documents effectively. We will discuss how these approaches can help to balance the competing goals of maximising access to information while minimising disclosure risks. We will also present a new freely available document collection that we have created to support researchers and practitioners. This resource is designed to help develop and evaluate AI systems for sensitivity classification and sensitivity‑aware search, with the goal of encouraging research in this important area and ultimately improving the safe and responsible public access to sensitive archives.
Bios:
Graham McDonald
Graham McDonald is a Senior Lecturer in Information Retrieval at the University of Glasgow. His research focuses on technology‑assisted sensitivity-aware retrieval and classification, safe information extraction, and responsible information retrieval for complex document collections. After completing his PhD in 2019 on Technology‑Assisted Sensitivity Review, which developed technologies for open government in the era of digital records, he led a number academic–industry knowledge exchange projects with SVGC Ltd. and FCDO Services to support the FCDO’s efficient execution of Digital Sensitivity Review through tailored state-of-the-art digital solutions. He was Principal Investigator of the EPSRC New Horizons project Safe Information Extraction from Patient Histories (SIEPH) and is currently a co-Investigator on the RAI UK project Participatory Harms Auditing Workbenches and Methodologies (PHAWM). Graham has held several significant leadership roles in the research community. He served as General Co‑Chair of ECIR 2024, Finance Chair for IEEE ICDCS 2025, co‑organised the NIST TREC Fair Ranking Track, delivered the Search Among Sensitive Content tutorial at ECIR 2021 and is co-organiser of the AI & Open Government Workshop at ICAIL 2026. He is an active member of the Information Retrieval community, serving on SPC and PC committees for leading conferences and journals, and has published more than 30 papers in these venues. He is also Vice Chair of the BCS Information Retrieval Specialist Group.
Iadh Ounis
Iadh Ounis is Professor of Information Retrieval in the School of Computing Science at the University of Glasgow. His research focuses on developing artificial intelligence technologies that support effective access to information, including advances in search engines, recommender systems, and conversational assistants. He is the principal investigator of the Terrier open‑source Information Retrieval platform and a contributor to its Python‑based AI extension, PyTerrier, widely used in academia and industry to support experimentation in AI‑driven search. A recipient of the UKeiG Tony Kent Strix Award, he has published over 300 papers in leading computer science venues, led several major international research initiatives, co‑chaired major international conferences including ECIR 2024 and IEEE ICDCS 2025, and led numerous UKRI, EU, and industry‑funded projects. His recent work includes contributions to AI‑supported sensitivity review for archives, collaborating with public sector and industry partners to help organisations manage and release information safely and responsibly. He has served in senior leadership roles within the Scottish Informatics and Computer Science Alliance (SICSA) and The Data Lab, the Scottish Funding Council Innovation Centre for data and AI, and is currently a member of The Guild’s EU Heads of Artificial Intelligence and Digital Research Group.