Organising Committee

Preface

Our view of artificial intelligence (AI) is the science of agents acting in the world. [1]. Agents receive precepts from the environment and take action. [2] An intelligent agent does an action with the aim to achieve a local optimum. Achieving a local optimum implies the focus on maximizing performance within a specific environment or task while recognizing that global optimization may be impractical or unnecessary in some cases. In many real-world scenarios, the goal is not absolutely perfect but "good enough" solutions that meet the needs of the current context.

AI is currently on a grand triumphant advance in all parts of society. This advance does not stop at the humanities. Humanities-Centred Artificial Intelligence (CHAI) was suggested as an emerging paradigm in the article [3], and in the fourth CHAI workshop, we will highlight human-machine interactions through a series of current research projects that emphasise the role of data usability, computational methods and the use of large language models (LLMs) [4,5] in various research areas, especially in the humanities and law.

The article [6] deals with the application of (intelligent) agents in the digital humanities, especially in the field of text analysis. It emphasises that such agents offer new possibilities for analysing and interpreting texts that complement and support the work of humanities scholars rather than replacing it. The focus of the CHAI contributions is also on complementing and supporting the work of humanities scholars. The use of intelligent agents in the humanities requires close collaboration between humanities scholars and AI experts, which can lead to new insights and methods in both fields, as the following articles also demonstrate.

The first paper Automate Text Processing for Schematically Analyzing Legal Texts presents an innovative approach to the use of LLMs for processing legal texts and addresses their limitations. Given the complexity and constant evolution of legal documents, the authors propose a method for automatically extracting schematic representations that enables intelligent agents to make informed decisions based on structured information. The method includes a legal case study and outlines a process for modelling and extracting these schemas using LLMs. The paper also evaluates the capabilities of ChatGPT and Gemini in this context. While the authors focus primarily on legal texts, they suggest that their approach could be adapted for different types of natural language texts to improve decision making in different domains.

The second article From Data Acquisition to Latent Semantic Analysis: Developing VERITRACE's Computational Approach to Tracing the Influence of Ancient Wisdom in Early Modern Natural Philosophy focuses on the application of latent semantic analysis (LSA) [7] to uncover historical connections and influences. This computational approach not only contributes to a better understanding of ancient philosophies, but also illustrates the broader implications of LSA when analysing large text corpora.

The third article Retrieving Information Presented on Webpages Using Large Language Models: A Case Study demonstrates the potential of LLMs in improving information retrieval from digital sources. This is in line with ongoing research on the potential of LLMs to improve the accessibility and usability of data in various domains.

The forth article Testing the Syntactic Competence of Large Language Models with a Translation Task includes a discussion of the use of translation tasks as a method for testing the syntactic competence of LLMs, particularly in the treatment of dative ambiguity in Russian. This research emphasises the importance of language processing in the evaluation of agents' LLMs and their ability to process complex linguistic structures.

In the fifth article Tracing the Palola Shahi Royal Genealogy by Fusing LLMs and Databases?: A Case Study, research into tracing royal genealogies, such as the Pal . ola S . āhi lineage (for more details see [8,9]), through the fusion of LLMs and databases illustrates the innovative applications of agents in historical research. This case study highlights the potential of interdisciplinary collaboration that combines computational techniques with historical research.

The first invited article Humanities in the Center of Data Usability: Data Visualization in Institutional Research Repositories sets the stage by emphasizing the critical need for effective data visualization techniques that enhance the reusability and interoperability of research datasets. In addition, an innovative citation approach is presented that makes it possible to refer not only to the entire repository, but also to a specific data set. The second invited article on Human-Centred Open-Source Automatic Text Recognition for the Humanities with OCR4all emphasizes the need for user-friendly tools that empower researchers in the humanities to a mass data analysis using software tools effectively. Both contributions are in line with the general trend towards the development of open source and generic solutions that improve the findability, accessibility, interoperability and reusability of data.