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Westonia: Selected Works of Elizabeth Jane Weston
No full textUse of this work governed by a CC BY-NC-SA license
Identity-related Speech Suppression in Generative AI Content Moderation
No full textAutomated content moderation has long been used to help identify and filter undesired user-generated content online. But such systems have a history of incorrectly flagging content by and about marginalized identities for removal. Generative AI systems now use such filters to keep undesired generated content from being created by or shown to users. While a lot of focus has been given to making sure such systems do not produce undesired outcomes, considerably less attention has been paid to making sure appropriate text can be generated. From classrooms to Hollywood, as generative AI is increasingly used for creative or expressive text generation, whose stories will these technologies allow to be told, and whose will they suppress?
In this paper, we define and introduce measures of speech suppression, focusing on speech related to different identity groups incorrectly filtered by a range of content moderation APIs. Using both short-form, user-generated datasets traditional in content moderation and longer generative AI-focused data, including two datasets we introduce in this work, we create a benchmark for measurement of speech suppression for nine identity groups. Across one traditional and four generative AI-focused automated content moderation services tested, we find that identity-related speech is more likely to be incorrectly suppressed than other speech. We find that reasons for incorrect flagging behavior vary by identity based on stereotypes and text associations, with, e.g., disability-related content more likely to be flagged for self-harm or health-related reasons while non-Christian content is more likely to be flagged as violent or hateful. As generative AI systems are increasingly used for creative work, we urge further attention to how this may impact the creation of identity-related content
White Camphor and Peppercorn Hair: Blackness in Medieval Arabo-Asia
No full textReading classical Arabic and Chinese sources at once comparatively and intersectionally, this article initiates an investigation of Black labor—specifically Black sailors and slaves—employed in medieval trade networks that connect Africa, Arabia, and Persia to South Asia, Southeast Asia, and China. It does not presume a homogeneous definition of Blackness, nor a generalized notion of slavery. While informed by concepts developed in scholarly studies of transatlantic slavery and Euro-American colonial history, this article strives to expand our understanding of the global articulation of Blackness beyond both the modern period and the Atlantic world. I draw on numerous genres of classical literature—Islamic ḥadīth commentaries, stories of marvels, geographical works, poetry, Buddhist dictionaries, and polemical treatises—and corroborate them with visual evidence from the same or adjacent periods. Rather than aiming for a social history of Black labor, I suggest that we harness the magical qualities of these half-true, half-invented narratives, capitalize on their marvelousness, and instead of laying claim to a definitive account of who the Black sailors were and what they did, create new avenues of research and imagination that may help us regain access to the breathtakingly rich and layered world of a Black subalternity articulated translingually across the medieval Indian Ocean world
Strategic acyl carrier protein engineering enables functional type II polyketide synthase reconstitution in vitro
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Resettled Iraqi refugees in the United States: War, refuge, belonging, participation and protest [book review]
No full textWilliam Williams, \u3cem\u3eProfessor of Fine Arts\u3c/em\u3e
No full textFlorence Griswold Museum. (February 22-June 22, 2025). Their Kindred Earth: Photographs by William Earle Williams. Old Lyme, CT. https://florencegriswoldmuseum.org/william-earle-williams/https://scholarship.haverford.edu/featuredfac/1189/thumbnail.jp
