Conference Agenda (All times are shown in Eastern Daylight Time)

The rapid expansion of content presents significant challenges in records management, notably in retention and disposition, appraisal, and organization. Our study highlights how integrating artificial intelligence (AI) into archival science can help address these issues. We begin with a thorough analysis of 45 papers published between 2011 and 2023 that met our predetermined criteria. All the articles were written in English; 40% of these were reviews, and the remaining 60% were original research articles. We investigated the key AI techniques and their applications in archives and records management functions. Our findings highlight key AI-driven strategies that promise to streamline recordkeeping processes and improve data retrieval in the immediate future. This review outlines the current state of AI in archival science and records management and lays the groundwork for integrating new techniques to transform archival practices. Our research emphasizes the necessity for enhanced interdisciplinary collaboration between AI experts and archival professionals.

Provenance documentation is essential for establishing the authority and trustworthiness of objects and data in different domains. However, provenance stories can be susceptible to misinterpretation, incompleteness, or biases, resulting in provenance failures. Extant research has attributed provenance failures to the absence or incorrectness of data. In this paper, we explore the idea of provenance failures and investigate the different types of failures in relation to our typology of provenance uses in research publications. Via case studies in natural history, scholarly communication, and cultural heritage, we posit that provenance failure is an underexplored and undertheorized concept and lay the groundwork of a theory of provenance failure.

Will continuous wars change social morality? While previous research focused on war ethics, in this study, by tracking large-scale historical newspapers, from the perspective of digital humanities, we provide evidence through big data analysis to answer this question. Leveraging the database the Late Qing and Republican-Era Chinese Newspaper and widely-used psychological moral lexicons, we retrace the diurnal dynamics of collective moral motivation in Chinese modern history from 1919/1/1 to 1949/9/30. Analyzing historical newspapers with moral lexicons, we track moral motivation across four wartime periods: Warlord Era, Agrarian War, Anti-Japanese War, and Liberation War. Statistics shown that moral motivation of Chinese society continuously increased as the wars dragged on. It is also discovered that the wars significantly motivated a higher level of agency motive than communion motive. As far as we know, this is the first empirical study from a digital humanities perspective that discusses the relationship between war and collective morality.

This paper proposes Humanities-in-the-Loop, a methodological framework that embeds close reading into each stage of the Retrieval-Augmented Generation (RAG) pipeline to enhance the processing of digital archival materials. This framework includes manual annotation, knowledge maintenance, reviewer validation, prompt engineering, and human interpretation. Taking the diaries of Coching Chu as a case study, the system addresses the limitations of conventional RAG methods in capturing the contextual complexity and historical nuance inherent in personal archives. An evaluation further confirms the effectiveness of this approach in delivering faithful, contextually grounded responses. The proposed framework not only enhances answer accuracy and interpretability but also enables traceable, human-centered inquiry in digital humanities research.