eabh (The European Association for Banking and Financial History e.V.) in cooperation with the Central Bank of Hungary (Magyar Nemzeti Bank)
13 June 2024, Budapest, Hungary
Call for Papers
It is fair to say that Artificial Intelligence (AI) is one of the most promising technological innovations of the last decade, and yet it is seen many as the most apocalyptic one. When asked about its own role for the archives of financial institutions, Chat GPT (Chat Generative Pre-Trained Transformer), a large language model-based chatbot developed by Open AI that launched on November 30, 2022, says:
Chat GPT
AI can assist historic archives of financial institutions by automating the process of data digitization, making it easier to access and search for historical financial records. It can also aid in the analysis of vast datasets, uncovering valuable insights and trends in financial history. Furthermore, AI can enhance security by detecting anomalies and potential fraud within the archives, helping safeguard sensitive financial information. Crucially, the answer depends on algorithm; meaning that answers may differ between users, but still is Chat GPT right here? What do the archivists say? Does AI have the potential to help better digitize, appraise, describe, and access historic structured datasets and unstructured data in archives consisting of textual records, images, and more?
This workshop is for archives practitioners, records managers, and information management professionals working in financial, public, and academic organisations. What values or characteristics of machine learning and artificial intelligence are most relevant to the archives profession? What can it potentially do for those wanting to preserve and access historic datasets or other archival holdings? Who in the sector is using AI already, why, and how? Will AI here optimise the archival, records and information management profession?
This meeting aims to explore case studies and experiments using AI in archives or other memory and heritage institutions. For example, using AI, machine learning or natural language processing for: improving digitization and text processing; data classification and descriptive metadata generation; content summarization or predictive analysis; detecting anomalies in large collections of structured and unstructured data; improving search and retrieval; scanning for data privacy or other security issues; workflow automation, particularly in preservation; and more.
We invite people from different sectors, fields, and departments together to talk about solutions for AI-related matters like confidentiality, privacy, security, copyright, technology, varieties of exciting structures and systems, ethics, skills, and potential partnerships. Our meeting will be an open space to hear from other archive professionals about the work they are already doing to experiment with AI now. Are there lessons learned yet? How can the archives profession embed AI into its practice, norms, policies, and strategies in the future?
Please send your proposals (1 page with short outline, plus link to your credentials) no later than 1 February 2024 to: c.hofmann@bankinghistory.org
The committee is formed by: Mike Anson (eabh); Carmen Hofmann (eabh); April Miller (The World Bank); Balázs Vonnák (Central Bank of Hungary) eabh (bankinghistory.org) is a membership-based organisation that maintains a global network of financial actioners, academics and archivists who together work on providing historical analysis of the financial sector.
