FIAT/IFTA World Conference 2024

The transformative potential of applying artificial intelligence in the context of audiovisual archiving has been demonstrated across a spectrum of use cases related to search & exploration, preservation, artistic expression and big-data analysis. This presentation explores research findings regarding the integration of AI solutions specifically in humanities and social sciences research.  

The emergence of Large Language Models (LLMs) and Large Multimedia Models (LMMs) poses new opportunities and questions in this area. For instance, LLMs could support the content annotation tasks for researchers working on framing analysis over large amounts of textual data. But more investigation is required to determine how such systems could be fine tuned and integrated into research workflows to deliver satisfactory and trustworthy outcomes. The goal of this research was to develop a roadmap that guides the development and use of LLMs and LMMs in SSH research. It (i) Identifies potential SSH research areas (such as framing analysis) that could benefit from the use of LMMs / LLMs, (ii) Identify limitations and risks of these techniques, (iii) provide recommendations on how LLMs and LMMs could be designed and fine tuned to fit SSH scholar needs.

We will also reflect on how researchers, archives, and the broader cultural sector can contribute to promoting more responsible AI practices in society and raising "AI Literacy." This entails exploring practical ways in which cultural institutions can actively engage with the ethical and societal implications of AI, such as curating exhibitions that examine AI's impact on human lives, hosting workshops to educate the public about AI technologies and their implications, and collaborating with researchers to develop resources that foster critical thinking about AI in diverse communities.