DataX

What is AI actually doing to teaching, learning, and research at the university? The third edition of this faculty seminar from NNAI Livescu and DataX brings together UCLA faculty and researchers across disciplines to share what they are experiencing with AI, from promising applications and unexpected discoveries to challenges, failures, and open questions. Through small-group discussion and exchange, participants will explore how AI is showing up in academic practice today and what its growing presence might mean for the university. Whether you’re skeptical, excited, overwhelmed, or simply curious, this session offers space for candid conversation grounded in real experiences rather than hype. In collaboration with UCLA AI Exchange.

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Abstract: In early modern Venice, sex workers were frequently accused of practicing magic and witchcraft, subsequently facing trial before the Holy Office. Drawing upon this empirical evidence, this project examines hundreds of Inquisition files spanning the years 1580 to 1630 to reconstruct these women's social networks. The objective is to contextualize and interpret the complex interrelationships between magic, sex work, and gender dynamics in early modern society. Adopting a microhistorical approach that integrates close reading with social network analysis and Gephi visualizations, this paper presents preliminary findings and discusses the methodological challenges of translating archival sources into digital data—particularly when researching the lives of marginalized, working-class women. Bio: Alessandra Celati is a research fellow in Early Modern History at the University of Turin, Italy. She earned her PhD from the University of Pisa and was a Marie Curie Global Fellow between Stanford University and the University of Verona, where she conducted research for several years. She is currently working on a project focused on "Love Magic and Sex Work in Early Modern Venice: A Network of Women in the Inquisition Records."

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Over the past 60 years, sociolinguists have documented variation in African Americans’ speech. This work has resulted in a large body of literature detailing the complex relations among language, gender, sexuality, race, power, and class. The development language technologies, such as automated speech recognition (ASR) and large language models, has raised new questions about dialect fairness and accessibility, which sociolinguistics is apt to address. In this talk, Zion Mengesha presents three case studies for the application of sociolinguistics to artificial intelligence. Using the dialect density measure, the first study shows that all five major speech recognizers misunderstood African American speakers up to two times more than white speakers, revealing how speech technologies reproduce standard language ideologies. The second study examines the psychological and behavioral consequences of dialect discrimination using video data collected over a 2- week diary study of African Americans’ interactions with voice technology. The final study shows linguistic consequences of ASR misrecognition, examining how African Americans modify their prosody and morphosyntax in order to be better understood. She concludes with a discussion on how to apply sociolinguistic insights about African American English (AAE) to artificial intelligence to advance technological justice for speakers of African American English and other minoritized language varieties. Brought to you by the Data Justice Pillar.

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