Conference Agenda

This study explores how AI technology threatens the professional identity of Archives and Records Management (ARM) practitioners in ESARBICA member states. Using the AI identity threat framework, it examines the influence of such threats on AI adoption intentions and provides actionable insights to foster collaboration between ARM professionals and AI systems.

The accelerating incorporation of artificial intelligence (AI) into Archives and Records Management (ARM) is introducing both opportunities and challenges that demand urgent scholarly attention. While much has been written about how AI transforms work processes, little is known about its impact on the professional identity of ARM practitioners. In particular, the intersection of AI-induced threats and the existing challenges of identity clarity within the ARM profession remains underexplored. With ARM professionals often grappling with a lack of recognition and ambiguity in their roles, this study aims to address this critical research gap by investigating how AI challenges the value, meaning, and enactment of professional identity and how these identity threats influence AI adoption intentions.

This study investigates the question, Who is an ARM professional in the presence of AI technology? Using the IT identity threat and AI identity threat framework, it explores how AI systems may devalue professional expertise, disrupt the meanings associated with ARM professional identity, and hinder practitioners’ ability to enact their roles. The research focuses on ARM professionals in ESARBICA member states, including Angola, Botswana, Kenya, Lesotho, Malawi, Mozambique, Namibia, Tanzania, South Africa, Swaziland, Zambia, Zanzibar, and Zimbabwe, where engagement in ARM-related activities and professional development is high.

Adopting a positivist paradigm, the study uses a quantitative approach, utilizing an online questionnaire based on validated AI identity threat constructs. Participants are selected through purposive, snowball, and convenience sampling to ensure a representative and diverse sample of ARM professionals in archives, records, and related fields. Data analysis is conducted using SPSS version 25, with descriptive statistics revealing the extent of AI identity threats and their influence on AI adoption intentions.

By addressing this gap in the literature, the study contributes to the archival futures discourse, highlighting the critical interplay between professional identity, technological innovation, and workforce integration. The findings aim to provide actionable insights to strengthen the professional identity of ARM practitioners, foster resilience against AI threats, and ensure the profession’s sustainability and relevance in a rapidly changing technological environment.

With the rapid expansion of AI infiltrating all sectors of society, archivists may find their involvement with AI happening in some unlikely ways. This presentation details a project that began in 2024 with RPI’s President creating the Project Bridge Generative AI Team and included the Institute Archivist at the helm of research and crafting a commencement speech for a woman who died in 1903. The project was highly successful but left the archivists pondering ethical and environmental concerns.

As Artificial Intelligence shapes the way we work and share information, archivists may find their involvement with AI happening in some unlikely and perhaps disconcerting ways. This presentation details a project that began in 2024 at Rensselaer Polytechnic Institute in Troy, N.Y. RPI’s President created the Project Bridge Generative AI Team and included the Institute Archivist at the helm of research to craft a commencement speech for a woman who died in 1903. The project was highly successful and ultimately garnered the attention of thousands, but left the archivists pondering ethical and environmental concerns.

Archivists at RPI were thrust into the world of prompt engineering, Large Language Models, and conducting primary source research to then train GPT-4 to generate text in the writing style and voice of Emily Warren Roebling (who died in 1903) renowned spouse of the Chief Engineer of the Brooklyn Bridge. This project was initiated in order to confer a posthumous honorary degree given at RPI’s 200th anniversary graduation ceremony. Intrigued that they were entrusted by their college president, and excited that the archives (and archivists work) was placed in the spotlight along with the opportunity to collaborate with a leader in AI research and a computer scientist, hindsight is 20/20. The archivists were left feeling concerned about AI’s environmental impact and the ways in which AI presents a new burgeoning ethical imperative for archivists.

As stewards of the historical record, archivists typically have the power of appraisal and decide who is remembered and why, but the utilization of AI presents new dilemmas and begs questions that need to be addressed. “How do we know we are imparting this same caution when it comes to using machine learning to interpret the human experience?” Furthermore, “What right do we have to consume finite natural resources in the process of using AI?”

While archivists are routinely asked to embrace new technologies, practitioners must come to the sober realization that generative AI is resource intensive by nature - in terms of mining raw materials for production, electricity used to power data centers, creation of harmful waste products (mercury and lead), and use of water to cool electronic components. Archivists must openly discuss this paradigm and become advocates for its sustainable use.

This presentation will address the myriad successes and drawbacks of using AI for short term archives projects, and leaves the listener to contemplate greater awareness that while AI may facilitate deeper understanding of collections, the technology needs further consideration both in terms of ethics and sustainability.

La implementación de la Inteligencia Artificial (IA) en las prácticas archivísticas tiene una alta potencialidad, al redefinir los procesos de gestión, preservación y acceso a la información. En tal sentido, este trabajo tiene como objetivo analizar los retos y oportunidades de la implementación de estas herramientas en procesos como la clasificación y descripción inteligente, así como en la mejora de la búsqueda documental. También se abordan los retos éticos, técnicos y formativos.

La inteligencia artificial (IA) se ha consolidado como una herramienta clave para transformar múltiples disciplinas, incluida la archivística. La interacción entre estos campos redefine la gestión, preservación y acceso a los recursos documentales, ampliando las posibilidades para abordar los desafíos asociados con el creciente volumen de información en la era digital. Este trabajo analiza el impacto de la IA en los procesos archivísticos, desde la automatización de tareas repetitivas hasta la implementación de sistemas avanzados para la clasificación, indexación y recuperación de documentos. Tecnologías como el aprendizaje automático, el procesamiento del lenguaje natural y la visión artificial ofrecen soluciones que mejoran la eficiencia y precisión en la gestión documental, permitiendo que los profesionales se centren en actividades estratégicas de mayor valor. Más allá de las ventajas, se examinan los retos y oportunidades que supone la integración de la IA en los archivos. Entre ellos se encuentran la dependencia de tecnologías específicas, los riesgos derivados de sesgos en los algoritmos y los dilemas éticos relacionados con el manejo de datos personales y la transparencia de los sistemas. Este análisis incluye también el impacto en los perfiles profesionales, subrayando la importancia de capacitar a los archivistas en competencias tecnológicas para que asuman un rol activo en esta transformación. La metodología incluye el análisis de buenas prácticas e iniciativas de implementación, sobre cuya base se plantean potencialidades en la implementación de la inteligencia artificial en las prácticas archivísticas, enfatizando en interrogantes sobre la preservación digital a largo plazo y la sostenibilidad de estas herramientas en contextos con recursos limitados, especialmente en América Latina. Como conclusiones se destacan rasgos del panorama integral de las oportunidades y los desafíos para una implementación responsable que fortalezca la labor archivística y respete principios esenciales como la autenticidad y el acceso universal. El enfoque presentado busca resaltar el potencial de la IA como aliada de la archivística contemporánea, promoviendo un diálogo interdisciplinario que permita superar las barreras actuales. Con esta perspectiva, se aspira a fomentar nuevas líneas de investigación y desarrollo para encontrar soluciones sostenibles que garanticen la preservación del patrimonio documental en un entorno crecientemente digital.

The integration of transformative technologies such as Artificial Intelligence (AI) and Machine Learning presents significant opportunities for digital preservation in developing countries. This study adopts a qualitative research approach to ascertain the preparedness of archivists. The data collected will be analyzed to determine the feasibility of transformative technology interventions. The findings of the study informs policy development for scalable and cost-effective AI adoption.

The integration of transformative technologies as artificial intelligence and machine learning presents opportunities to combat challenges of limited funding, infrastructure, digital divide and the understanding of multilingual archives in developing countries archives. The study would adopts a qualitative research design with focus on content analysis and interviews to assess the preparedness of archivist for artificial intelligence interventions. The data collected would be analyzed to determine the extent to which transformative technologies are perceived as feasible solutions and barriers to its integration. Based on the findings of the study, the researcher would propose interventions and policy development for scalable and cost effective AI preservation interventions in transforming digital archives of the future in developing countries. Recommendations for fostering local and international collaboration for archivist would be suggested based on analyzed data. This study adds to literature on the archival tools, knowledge and strategies needed for digital preservation ensuring that developing countries archives are accessible for future generation.

Keywords: Digital Preservation, Archives, Artificial Intelligence, machine learning, Developing countries