NLP task	Stated motivation	Categories
Motivations	Techniques
Language modeling (Bordia and Bowman, 2019)	“Existing biases in data can be amplified by models and the resulting output consumed by the public can influence them, encourage and reinforce harmful stereotypes, or distort the truth. Automated systems that depend on these models can take problematic actions based on biased profiling of individuals.”	Allocational harms, stereotyping	Questionable correlations
Sentiment analysis (Kiritchenko and Mohammad, 2018)	“Other biases can be inappropriate and result in negative experiences for some groups of people. Examples include, loan eligibility and crime recidivism prediction systems...and resume sorting systems that believe that men are more qualified to be programmers than women (Bolukbasi et al., 2016). Similarly, sentiment and emotion analysis systems can also perpetuate and accentuate inappropriate human biases, e.g., systems that consider utterances from one race or gender to be less positive simply because of their race or gender, or customer support systems that prioritize a call from an angry male over a call from the equally angry female.”	Allocational harms, other representational harms (system performance differences w.r.t. text written by different social groups)	Questionable correlations (differences in sentiment intensity scores w.r.t. text about different social groups)
Machine translation (Cho et al., 2019)	“[MT training] may incur an association of gender-specified pronouns (in the target) and gender-neutral ones (in the source) for lexicon pairs that frequently collocate in the corpora. We claim that this kind of phenomenon seriously threatens the fairness of a translation system, in the sense that it lacks generality and inserts social bias to the inference. Moreover, the input is not fully correct (considering gender-neutrality) and might offend the users who expect fairer representations.”	Questionable correlations, other representational harms	Questionable correlations
Machine translation (Stanovsky et al., 2019)	“Learned models exhibit social bias when their training data encode stereotypes not relevant for the task, but the correlations are picked up anyway.”	Stereotyping, questionable correlations	Stereotyping, other representational harms (system performance differences), questionable correlations
Type-level embeddings (Zhao et al., 2018b)	“However, embeddings trained on human-generated corpora have been demonstrated to inherit strong gender stereotypes that reflect social constructs....Such a bias substantially affects downstream applications....This concerns the practitioners who use the embedding model to build gender-sensitive applications such as a resume filtering system or a job recommendation system as the automated system may discriminate candidates based on their gender, as reflected by their name. Besides, biased embeddings may implicitly affect downstream applications used in our daily lives. For example, when searching for ‘computer scientist’ using a search engine...a search algorithm using an embedding model in the backbone tends to rank male scientists higher than females’ [sic], hindering women from being recognized and further exacerbating the gender inequality in the community.”	Allocational harms, stereotyping, other representational harms	Stereotyping
Type-level and contextualized embeddings (May et al., 2019)	“[P]rominent word embeddings such as word2vec (Mikolov et al., 2013) and GloVe (Pennington et al., 2014) encode systematic biases against women and black people (Bolukbasi et al., 2016; Garg et al., 2018), implicating many NLP systems in scaling up social injustice.”	Vague	Stereotyping
Dialogue generation (Liu et al., 2019)	“Since the goal of dialogue systems is to talk with users...if the systems show discriminatory behaviors in the interactions, the user experience will be adversely affected. Moreover, public commercial chatbots can get resisted for their improper speech.”	Vague/unstated	Stereotyping, other representational harms, questionable correlations