S3 #44 How should machines translate sensitive language? Brain-to-brain with Sabrina Frohn.

Sabrina’s linkedin: https://www.linkedin.com/in/sabrina-frohn/ Papers: not THE implicit bias paper but one explaining implicit and explicit bias: “Social Justice in Our Minds, Homes, and Society: The Nature, Causes, and Consequences of Implicit Bias” by Laurie A. Rudman, 10.1023/B:SORE.0000027406.32604.f6 about the implicit association test I mentioned: https://www.projectimplicit.net/nosek/iat/default.html (I was not able to find the study I participated in, but I assume it is similar to this, perhaps was even based on this.) bias in machine translations: „Gender Bias in Machine Translation Systems“ Stefanie Ullmann et al., isbn: 978-3-030-88615-8 “What about em? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns, Lauscher et al., 10.48550/arXiv.2305.16051 comparing LLM and MT “Benchmarking Machine Translation with Cultural Awareness” by Binwei Yao, 10.48550/arXiv.2305.14328 „Evaluating Gender Bias in Machine Translation“, Stanovski et al., 10.18653/v1/P19-1164 machtsprache: https://www.machtsprache.de/ macht.sprache plugins: chrome: https://chromewebstore.google.com/detail/machtsprache-for-sensitiv/dichlnekfmanlagciihdnkgiefppilol firefox: addons.mozilla.org/en-GB/firefox/addon/macht-sprache/ interesting reads: „The complexities of linguistic discrimination“, Drożdżowicz et al., 10.1080/09515089.2024.2307993 “On the Translation of Otherness: The Univocal Case of Will Grayson, Will Grayson”, Badenes, 10.7202/1068906ar “Word embeddings quantify 100 years of gender and ethnic stereotypes”, Garg et al., 10.1073/pnas.1720347115 https://pocolit.com/ Sabrina’s paper: https://publications.waset.org/10014353/bibtex Podcast Credits: Produced by: Imogen Hüsing, Clara Kühne, Sophie Kühne, Sönke Lülf and Elisa Palme Logo by: Annika Richter Music by: Jan-Luca Schröder Write us an email to: [email protected] Contact us on Instagram: @kaleidoscience_pod