AI AND THE DIGITAL HUMANITIES

The Digital Humanities (DH), an interdisciplinary domain of study and research, comprises multiple fields energized by productive cross-fertilizations as it explores the innovative use of technology and computation in arts and humanities research both as a method of inquiry and as a means of dissemination. It channels researchers' interest in rethinking the humanities in new and creative ways.

This in turn leads to highly collaborative initiatives that foster connections as well as new and diverse relationship across disciplines within a large part of the academic institution, as well as worldwide.

DH primarily involves:

• Digitizing materials: Putting materials into digital formats so they can be processed computationally. 
• Processing: Using computational tools and methods to analyze large amounts of data.  See Video: Digital Humanities: How to Use Data in the Humanities (Dec 2, 2020; 1 hr3 mins).
• Presenting findings: To effectively present your findings in the digital humanities, you can utilize a variety of digital formats like interactive websites, data visualizations, digital exhibits, multimedia presentations, and scholarly articles. You need to ensure that your results are accessible, engaging, and that they clearly explain your methodology, your data analysis, and your key conclusions. Highlighting the unique insights gained from using digital tools and methods is essential.

To utilize AI in the digital humanities (DH), you need (a) access to large datasets of digitized humanities materials like texts, images, audio, or video, (b) computational methods like machine learning and natural language processing, and (c) the ability to critically interpret the AI-generated insights to ensure they align with the historical and cultural context. You also need detailed metadata describing the content, and a strong understanding of the specific humanities field you're studying,

Key components of AI in the DH include:

• Digitized Humanities Data such as:

-- Audio recordings like speeches, interviews, and music; 

-- Metadata: detailed information about the data like author, date, location, and subject matter; 

-- Textual sources like historical documents, literary works, and personal letters;  

-- Visual data like paintings, photographs, and artifacts.

AI Techniques:

--  Natural Language Processing (NLP): Analyzing and extracting meaning from text, including sentiment analysis, topic modeling, and named entity recognition. 

- Machine Learning algorithms: Supervised learning for classification tasks (e.g., identifying themes, genres), and unsupervised learning for pattern discovery. 

-- Computer Vision: a field of computer science that focuses on enabling computers to analize visual data to identify objects and people, scenes, and patterns within images and videos.  

Humanities Expertise

Deep knowledge of the specific historical period, cultural context, and disciplinary frameworks relevant to your research in order to critically interpret the AI-generated results and contextualize them within the humanities field. 

Essential considerations when using AI in digital humanities:

-- Data quality and bias: Ensuring that the data used to train AI models is representative and diverse to avoid biased results. 

-- Ethical implications: Being mindful of potential biases in AI algorithms and ensuring responsible use of technology. 

-- Interpretability: Understanding how AI models reach their conclusions to validate their findings especially when a model's decisions or conclusions could have serious consequences.