Co-authored by a Computer Scientist and a Digital Humanist, this article examines the challenges faced by cultural heritage institutions in the digital age, which have led to the closure of the vast majority of born-digital archival collections. It focuses particularly on cultural organizations such as libraries, museums and archives, used by historians, literary scholars and other Humanities scholars. Most born-digital records held by cultural organizations are inaccessible due to privacy, copyright, commercial and technical issues. Even when born-digital data are publicly available (as in the case of web archives), users often need to physically travel to repositories such as the British Library or the Bibliothèque Nationale de France to consult web pages. Provided with enough sample data from which to learn and train their models, AI, and more specifically machine learning algorithms, offer the opportunity to improve and ease the access to digital archives by learning to perform complex human tasks. These vary from providing intelligent support for searching the archives to automate tedious and time-consuming tasks. In this article, we focus on sensitivity review as a practical solution to unlock digital archives that would allow archival institutions to make non-sensitive information available. This promise to make archives more accessible does not come free of warnings for potential pitfalls and risks: inherent errors, "black box" approaches that make the algorithm inscrutable, and risks related to bias, fake, or partial information. Our central argument is that AI can deliver its promise to make digital archival collections more accessible, but it also creates new challenges - particularly in terms of ethics. In the conclusion, we insist on the importance of fairness, accountability and transparency in the process of making digital archives more accessible.

Lifelogging is referred to as the process of automatically capturing the everyday activities of an individual to ultimately create a digital diary for further sharing, which can be challenging to manage and retrieve due to its multimodal nature. Lifelog retrieval systems not only have the potential to transform the way people interact and understand their lives, but also provide insights into their behaviour, habits, and preferences. The Lifelog Search Challenge (LSC) is a live benchmarking challenge to evaluate the performance of lifelog search tools in a real-time. This paper describes the modifications made to the E-LifeSeeker retrieval system, which participates in the 6th LSC challenge. This year, we enhance not only the underlying core engine with the latest pre-trained embedding models but also the user interface to be more intuitive for novice users. Moreover, Differential Networks are implemented to address the new question-answering task this year. These new modalities are designed to provide users with a more intuitive and efficient search experience, easing the process of locating information needed from the huge collection of egocentric lifelog images.