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Critical Practice in Text Data Mining Research Cluster, 2020-2021 Project Report
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Data from: Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types ...
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Abstract:
Concerns about gender bias in word embedding models have captured substantial attention in the algorithmic bias research literature. Other bias types however have received lesser amounts of scrutiny. This work describes a large-scale analysis of sentiment associations in popular word embedding models along the lines of gender and ethnicity but also along the less frequently studied dimensions of socioeconomic status, age, physical appearance, sexual orientation, religious sentiment and political leanings. Consistent with previous scholarly literature, this work has found systemic bias against given names popular among African-Americans in most embedding models examined. Gender bias in embedding models however appears to be multifaceted and often reversed in polarity to what has been regularly reported. Interestingly, using the common operationalization of the term bias in the fairness literature, novel types of so far unreported bias types in word embedding models have also been identified. Specifically, the ... : This data set has collected several popular pre-trained word embedding models. -Word2vec Skip-Gram trained on Google News corpus (100B tokens) https://code.google.com/archive/p/word2vec/ -Glove trained on Wikipedia 2014 + Gigaword 5 (6B tokens) http://nlp.stanford.edu/data/glove.6B.zip -Glove trained on 2B tweets Twitter corpus (27B tokens) http://nlp.stanford.edu/data/glove.twitter.27B.zip -Glove trained on Common Crawl (42B tokens) http://nlp.stanford.edu/data/glove.42B.300d.zip -Glove trained on Common Crawl (840B tokens) http://nlp.stanford.edu/data/glove.840B.300d.zip -FastText trained with subword infomation on Wikipedia 2017, UMBC webbase corpus and statmt.org news dataset (16B tokens) https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M-subword.vec.zip -Fastext trained with subword infomation on Common Crawl (600B tokens) https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M-subword.zip" ...
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Keyword:
age; algorithmic bias; Bias; gender bias; physical appearance; political leaning; religious sentiment; sexual orientation; Socioeconomic status; word embedding models
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URL: https://dx.doi.org/10.5061/dryad.rbnzs7h7w
http://datadryad.org/stash/dataset/doi:10.5061/dryad.rbnzs7h7w
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Automated Journalism as a Source of and a Diagnostic Device for Bias in Reporting
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In: Media and Communication ; 8 ; 3 ; 39-49 ; Algorithms and Journalism: Exploring (Re)Configurations (2020)
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Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda
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On the integration of linguistic features into statistical and neural machine translation
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In: Vanmassenhove, Eva Odette Jef orcid:0000-0003-1162-820X (2019) On the integration of linguistic features into statistical and neural machine translation. PhD thesis, Dublin City University. (2019)
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From Big Data to Deep Learning: A Leap Towards Strong AI or ‘Intelligentia Obscura’?
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In: Big Data and Cognitive Computing ; Volume 2 ; Issue 3 (2018)
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