Chude-Sokei explores the historical connections between race and technology. He looks back to the time of the transatlantic trade in enslaved people and works his way through various historical stages to current issues surrounding artificial intelligence.
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Reading through various studies on gender bias in machine translation, I stumble across the sentence: The doctor asked the nurse to help her. It’s used in a study that tests how gender is translated from English into languages which, unlike English, have grammatical gender. This attribution is particularly relevant when it comes to terms that label people. In English, for example, doctor is gender-neutral, whereas in German one would traditionally have to choose between ‘Arzt’ or ‘Ärztin’, the former a male doctor, the latter female. Intrigued, I open one of the most popular translation engines to see what happens when I translate this sentence into German.
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As part of our macht.sprache. / case.sensitive. project, we’re speaking to various experts who deal with language, translation or artificial intelligence. Kenza Ait Si Abbou explains some of the challenges in the field of artificial intelligence to us (e.g. in form of unconscious bias) but always stays solution-oriented.
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This is the second time that we share a peek behind the scenes of our project macht.sprache. This time we explain some of the thoughts that went into the development of a code of conduct, the assessment system for translation examples, and how we’re hoping to motivate people to participate.
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