Counter Factual Data Augmentation for Mitigating Gender Bias in Morphologically Rich Languages

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One of the biggest challenges facing modern machine learning systems is removing bias from data. As AI algorithms areincreasingly being used to shape and determine important decisions in private and public life, it is essential to ensure thatthese systems are devoid of human prejudice and error. Nonetheless, the utility and accuracy of these algorithmsdepend on data which "are not objective; they are creations of human design" (Crawford, 2013). One area in which datais inherently biased is language. My current Master's thesis aims to create a novel generative model that transformssentences of one gender form into another gender form, for languages such as Spanish and Hebrew which possessmasculine and feminine inflections for nouns, verbs, and adjectives. The PhD will seek to build on this research andinvestigate three main questions: (1) To what extent can the efficacy and efficiency of the generative model used tobalance gendered language be strengthened and enhanced? (2) What other techniques can be designed andimplemented to de-bias corpora in English and other languages? And (3), to what extent can these models andtechniques be applied to study and correct other types of biases, such as racial and cultural biases? The PhD will aim tobuild systems that will avoid bias amplification, facilitate interdisciplinary applications, and offer robustness to futuremodels developed for NLP tasks.