Deep Learning for Digital Humanists: Part II

In this interactive two-part virtual workshop, participants will learn more about the possibilities and ethical pitfalls in using artificial intelligence methods for humanities research.

In the last year, artificial intelligence technologies have skyrocketed in popularity. Many fall under the heading 'deep learning', a kind of artificial intelligence in which computers learn to extract statistical patterns from large collections of data. Perhaps the most jarring aspect of recent deep AI technology is not its unparalleled number-crunching abilities but the fact that it acts on human cultural artifacts--for example, ChatGPT acts on text, while Midjourney and DALL-E act on images. This is a continuing source of both intrigue and unease for academics, as deep AI is bound to both pioneering research and pressing ethical concerns. This workshop series engages both sentiments. Part 1 will sketch the current landscape of deep learning in digital humanities research, identifying major research questions, methodologies, and conclusions. Part 2 considers the problems in using deep learning to explore human-made works of art, ethical practices in deep learning for digital humanities research, and knowledge gaps in AI ethics.

Note: this workshop will involve a lot of working together, but we do not expect participants to bring existing knowledge of DH methods, programming, or technical skills in general. The workshop is open to current Yale graduate students, postdocs, faculty, and staff.

WORKSHOP LEADER BIO
Nicole Cosme-Clifford is a fourth year PhD candidate in the Music Department, and a consultant for the Yale Digital Humanities Lab and the Graduate Writing Lab. Her research interests include AI explainability in music scholarship, AI's relationship to contemporary music-makers and music economies, and vernacular music traditions in the United States.

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