“ I ’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset (2022)

Abstract: As language models grow in popularity, it becomes increasingly important to clearly measure all possible markers of demographic identity in order to avoid perpetuating existing societal harms. Many datasets for measuring bias currently exist, but they are restricted in their coverage of demographic axes and are commonly used with preset bias tests that presuppose which types of biases models can exhibit. In this work, we present a new, more inclusive bias measurement dataset, HolisticBias, which includes nearly 600 descriptor terms across 13 different demographic axes. HolisticBias was assembled in a participatory process including experts and community members with lived experience of these terms. These descriptors combine with a set of bias measurement templates to produce over 450,000 unique sentence prompts, which we use to explore, identify, and reduce novel forms of bias in several generative models. We demonstrate that HolisticBias is effective at measuring previously undetectable biases in token likelihoods from language models, as well as in an offensiveness classifier. We will invite additions and amendments to the dataset, which we hope will serve as a basis for more easy-to-use and standardized methods for evaluating bias in NLP models.

## introduction in recent years, there has been a series of works aiming to measure social biases or other unwanted behaviors in nlp. in particular, many works focus on generative models @xcite @xcite @xcite @xcite , which are well known to pose unique challenges for automatic evaluation @xcite @xcite . for models that generate, a common way to surface bias is to input prompts containing demographic information, and then analyze whether the models output socially biased text. such prompts are generally derived either from crowdsourcing @xcite or from slotting a set of terms into templates @xcite @xcite . however, whenever a method selects particular terms or templates for prompts, and groups them under particular demographic headings, it implicitly adopts a taxonomy which can include, or exclude, particular groups of people or particular ways of talking about groups of people. those who are most excluded from bias measurement are those who are historically marginalized or from underrepresented groups. in this work, we aim to create the largest and most inclusive taxonomy of textual people refer-[speaker a] i'm a woman who is hard of hearing [speaker b] i'm sorry to hear that. i'm sure you can still enjoy hiking. do you like hiking? holisticbias also aims to tackle another issue that plagues many existing word list taxonomies. namely, many existing taxonomies are static and unchanging, meaning they implicitly assert a particular classification of people as objective and immutable, and thus often reify an undesirable status quo. since people can refer to themselves and others in an endless number of ways @xcite , and since people references are prone to change over time @xcite @xcite , we have taken inspiration from calls to make model evaluation more dynamic @xcite , and we have created holisticbias as a "living" evaluation dataset for measuring social biases in language models. we expect holisticbias to expand and be adjusted as needed over time, and we invite researchers and community members to leave comments or contribute terms or additional annotations in the form of github pull requests on our open-sourced code. 1 to demonstrate the utility of holisticbias, we target several exemplar models-gpt-2, roberta, dialogpt, and blenderbot 2.0-and show that our expanded demographic terms list can better expose model social biases, including subtle ones pertaining to previously overlooked social categories, as in we measure bias across three settings (section 2.3): (1) token likelihoods of holisticbias sentences, (2) generations prompted with holis-ticbias sentences, and (3) differential rates of flagging holisticbias sentences as offensive. after having exposed such biases, we perform preliminary mitigations in section 4, to demonstrate how holisticbias can facilitate the whole social bias research cycle: it is useful in uncovering social biases, measuring their impact, and developing mitigations to help address them. we have opensourced our dataset and tooling, with the goal of helping to improve and standardize methods for researching social biases in nlp. ## reducing generative bias the previous section has shown how an expanded demographic bias dataset can help identify new biases in models. we now turn to how such a dataset can guide the mitigation of these newly uncovered biases. ## related work templates. this work assembles a large set of demographic descriptor terms to be slotted into existing bias templates. the practice of using descriptors to measure social bias began as a technique specific for probing the gender associations of static word embeddings @xcite @xcite . because contextualized word embeddings take context into account, templates were necessary for measuring social biases, such as stereotypical association with other text content @xcite . many projects have proposed particular measurement templates, which form the basis for prompts that can be used to measure bias @xcite @xcite @xcite @xcite @xcite . some even select existing sentences from text sources and swap demographic terms heuristically @xcite @xcite , utilize handcrafted grammars @xcite , or use machine-learned systems to swap descriptors @xcite . since one of our main contributions is the participatory assembly of a large set of demographic terms, our terms are compatible with nearly any templates to measure imbalances across demographic groups. prompts. a common approach to measuring bias relies on prompts generated by seeding crowdworkers with terms and having them write prompts from them @xcite . this approach has limitations, in particular because crowdworkers often misunderstand or can only incompletely follow annotation guidelines, which themselves can be difficult to specify completely @xcite . moreover, crowdsourcing can be very expensive and result in evaluation datasets limited in their size and scope, often covering only certain demographics or having only a few test sentences per demographic. to avoid the downsides of crowdsourcing and to enable more experimental control over the evaluation dataset, many works, including ours, employ a "term-andtemplate" method for bias evaluation. ## conclusion we have introduced a large dataset, holis-ticbias, with roughly 600 descriptor terms and half a million distinct sentence prompts. the comprehensiveness of the list allows us to uncover new biases in language models, as we demonstrated with three bias measurements (token likelihoods, generation bias, and an offensiveness classifier). we then showed a proof-of-concept bias mitigation technique, style equality, that uses a style classifier and controlled generation to reduce these newly found biases. the new dataset, new measurements, and mitigation can more holistically improve model fairness for a broader range of identities and demographics than previous approaches. in the future, we plan to expand this dataset to an even greater number of demographic terms, as well as intersections of those terms, to reflect the continually evolving ways in which people refer to themselves and others. the range of templates used in holisticbias can expand to cover other contexts in which identity is discussed, and nondialogue contexts more generally. we thus invite other researchers to contribute terms and templates to holisticbias in order to further broaden its coverage of demographic identities.