DOI: 10.5061/dryad.rbnzs7h7w
title: Data from: Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types

description: 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 popular embedding models analyzed here display negative
 biases against middle and working-class socioeconomic status, male
 children, senior citizens, plain physical appearance and intellectual
 phenomena such as Islamic religious faith, non-religiosity and
 conservative political orientation. Reasons for the paradoxical
 underreporting of these bias types in the relevant literature are probably
 manifold but widely held blind spots when searching for algorithmic bias
 and a lack of widespread technical jargon to unambiguously describe a
 variety of algorithmic associations could conceivably be playing a role.
 The causal origins for the multiplicity of loaded associations attached to
 distinct demographic groups within embedding models are often unclear but
 the heterogeneity of said associations and their potential multifactorial
 roots raises doubts about the validity of grouping them all under the
 umbrella term bias. Richer and more fine-grained terminology as well as a
 more comprehensive exploration of the bias landscape could help the
 fairness epistemic community to characterize and neutralize algorithmic
 discrimination more efficiently.
description: 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/<br> <br> -Glove trained on Wikipedia 2014 + Gigaword 5 (6B tokens)<br> http://nlp.stanford.edu/data/glove.6B.zip<br> <br> -Glove trained on 2B tweets Twitter corpus (27B tokens)<br> http://nlp.stanford.edu/data/glove.twitter.27B.zip<br> <br> -Glove trained on Common Crawl (42B tokens)<br> http://nlp.stanford.edu/data/glove.42B.300d.zip<br> <br> -Glove trained on Common Crawl (840B tokens)<br> http://nlp.stanford.edu/data/glove.840B.300d.zip<br> <br> -FastText trained with subword infomation on Wikipedia 2017, UMBC webbase corpus and statmt.org news dataset (16B tokens)<br> https://dl.fbaipublicfiles.com/fasttext/vectors-english/wiki-news-300d-1M-subword.vec.zip<br> <br> -Fastext trained with subword infomation on Common Crawl (600B tokens)<br> https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M-subword.zip"
description: The python library gensim (https://radimrehurek.com/gensim/)
 contains several utility functions to read this data set
 The file
 "fastext-common-crawl-300d-2M-subword-gensim-keyedvectors-format.rar" contains the fasttext model in word2vec format, which can be loaded with gensim. Following is a code snippet for loading the data: from gensim.models import KeyedVectors<br> modelsPath = os.path.normpath(r'PATH TO FILE')<br> modelFilePath = os.path.join(modelsPath,<br> 'fastext-common-crawl-300d-2M-subword.bin')<br> model = KeyedVectors.load(modelFilePath)