When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People? (2020)

Abstract: Social biases are encoded in word embeddings. This presents a unique opportunity to study society historically and at scale, and a unique danger when embeddings are used in downstream applications. Here, we investigate the extent to which publicly-available word embeddings accurately reflect beliefs about certain kinds of people as measured via traditional survey methods. We find that biases found in word embeddings do, on average, closely mirror survey data across seventeen dimensions of social meaning. However, we also find that biases in embeddings are much more reflective of survey data for some dimensions of meaning (e.g. gender) than others (e.g. race), and that we can be highly confident that embedding-based measures reflect survey data only for the most salient biases.

## introduction in april of 2015, protests erupted over the murder of freddie gray, jr. questions about what to call those protesting quickly became the focus of a national debate. in an interview on cnn with erin burnett, 1 baltimore city councilman carl stokes admonished then-president barack obama and then-mayor stephanie rawlings-blake for using the word thugs to refer to the protesters. burnett challenged stokes' admonition, claiming the protesters were indeed thugs because "they know it's wrong to steal and burn." stokes responded by stating the protesters were "...children who have been set aside @xcite marginalized." the argument between burnett and stokes is over the way we label people, the meanings of those labels, and the impacts these meanings can have. councilman stokes wants to avoid using the label "thug" because of its established, negative the scale is different for each dimension, e.g. for evaluation, survey participants responded with their belief as to whether people who held the identity were more likely to be a bad (further left on the x-axis) or good (further right). the beliefs we have about people who hold particular identities @xcite are important, because they often determine the behaviors we take towards people who are labeled with those identities @xcite . 2 for example, as councilman stokes knows, we do not behave the same way towards children as we do towards thugs. this is because, as reflected in figure 1 , people generally believe that children are weak and good, whereas thugs are bad and powerful. this leads us to want to do things like help children, versus wanting to attack thugs @xcite . however, measuring beliefs is difficult. traditionally, we have relied on surveys to collect these measurements. but there are tens of thousands of identities @xcite , and beliefs about them can form along many different dimensions of sociocultural meaning (e.g. gender, race, and others displayed in figure 1 ). measuring beliefs about many identities, on many dimensions, using traditional surveys can therefore be difficult. further, measuring the evolution of beliefs is often impossible with surveys, because survey data is extremely sparse historically @xcite . finally, measuring how these beliefs change with additional contextual information (e.g. beliefs about specific teachers, rather than teachers in general) is notoriously difficult with survey data @xcite . recognizing these difficulties, scholars have begun to develop nlp tools to measure beliefs about identities historically, at scale, and in context @xcite @xcite @xcite . most recent methods derive these measures by manipulating word embeddings. studying beliefs enmeshed in word embeddings is also critical because embeddings are widely used in downstream nlp models, which are themselves beginning to label people, for example, as job-worthy or not @xcite . measuring beliefs about people using embeddings therefore serves the dual purpose of understanding human biases and of ensuring such biases are not propelled further along by algorithms. however, work remains to understand when embedding-based measures of beliefs about identities accurately reflect more traditional survey measures, and why some beliefs may be reflected more accurately than others. the present work combines new and existing survey data with an extensive set of embedding-based measurement strategies to explore this at both the dimension level and the belief level. at the dimension level, for example, we ask, how well do embeddings capture beliefs about gender, relative to race? and if differences exist, why? such issues have arisen in existing work, for example, where @xcite see correlations of .65 between embedding-based and survey-based measures of beliefs about gender, but only .15 for ethnicity-based beliefs. at the beliefs-level, we ask, for example, how much more accurately do we capture beliefs about the potency (strength) of thugs, relative to beliefs about the potency of children? accuracy at this level is critical for linking historical trends in social behavior to societal-level beliefs about particular identities. our primary contributions are as follows: ## related work our work is grounded in literature on measuring beliefs about identities in social psychology in general and, more specifically, via word embeddings. we address these two literatures separately here. ## survey data we collect two new survey datasets for this paper. the first measures beliefs about the 57 identities foot_1 in we used a cluster-based approach to select the 57 identities we study. we study nine sets of six identities, where each set has been shown in prior work to be related in some way. five of the sets are characterized by a salient association to a specific institution described by mackinnon and @xcite . three sets are characterized by salient trait similarities and differences on gender, age or race/ethnicity. and one set of identities is included where all identities have strong negative evaluations. finally, we include three random identities as a mechanism for comparison in other work not described here. for further details on the selection criteria, survey populations, and results, see the appendix. ## methods our primary research question is, how accurately can we recover beliefs measured using surveys with word-embedding based measures? we study this first at the dimension level, i.e., how accurately do embedding-based measures reflect survey data across a set of identities on a given dimension of social meaning? we then study accuracy at the belief level, i.e., how accurately do embedding-based measures reflect survey data for specific identities on specific dimensions? our approach is straightforward, but is best explained by introducing some additional notation, provided in ## conclusion in this paper, we asked, can we trust measures of beliefs about people derived from word embeddings? we find the answer to be yes, at least on average. depending on one's perspective, this could be good or bad. from a cultural studies/social psychological perspective, this positive correlation further validates efforts to use word embeddings to study perceptions of people historically, at scale, and in context. on the other hand, from the "bias" per-spective, this suggests that a vast array of social biases are encoded in embeddings. however, we also find that some beliefs-specifically, extreme beliefs on salient dimensions -are easier to measure than others. more generally, across four datasets, we find that what we measure is more important than how we measure it. again, two different perspectives on this are needed. with respect to the study of culture and human stereotypes, we may be safest in studying only the most extreme results from embedding models, as has been done by, e.g., @xcite . from the bias perspective, given the rash of recent work on debiasing word embeddings, our results suggest that much more attention needs to be paid to how we are evaluating these approaches. currently, upstream evaluations of debiasing are centered almost exclusively on occupational identities on gender, where some of the most salient social biases we know of exist @xcite . others have argued that removing these salient beliefs may not remove gender information from embeddings @xcite . but gonen and goldberg's 2019 argument relies on a technical deficiency of existing approaches. we can make a similar critique by simply changing what is being measured. for example, the correlation between gender beliefs and the gender direction in the hard-debiased embeddings of @xcite is 0.05 (@xmath0.84) using identities in their data, and 0.4 (p <.05) using the identities in our data. similarly, removing gender bias does not remove bias on other dimensions. for example, while @xcite show that the num-berbatch embeddings harbor the least gender bias, we find that they are the only embedding to show consistently high correlations with age, leading to the potential for ageism downstream. more generally, stereotypes exist along a network of beliefs @xcite reflecting unwarranted correlations between many dimensions @xcite ; we must therefore be careful not to expect that removing meaning along one dimension will expel social biases from our models.