D eb IE : A Platform for Implicit and Explicit Debiasing of Word Embedding Spaces (2021)

Abstract: Recent research efforts in NLP have demonstrated that distributional word vector spaces often encode stereotypical human biases, such as racism and sexism. With word representations ubiquitously used in NLP models and pipelines, this raises ethical issues and jeopardizes the fairness of language technologies. While there exists a large body of work on bias measures and debiasing methods, to date, there is no platform that would unify these research efforts and make bias measuring and debiasing of representation spaces widely accessible. In this work, we present DebIE, the first integrated platform for (1) measuring and (2) mitigating bias in word embeddings. Given an (i) embedding space (users can choose between the predefined spaces or upload their own) and (ii) a bias specification (users can choose between existing bias specifications or create their own), DebIE can (1) compute several measures of implicit and explicit bias and modify the embedding space by executing two (mutually composable) debiasing models. DebIE’s functionality can be accessed through four different interfaces: (a) a web application, (b) a desktop application, (c) a REST-ful API, and (d) as a command-line application. DebIE is available at: debie.informatik.uni-mannheim.de.

## introduction ethical and fair natural language processing is an essential precondition for widespread societal adoption of language technologies. in recent years, however, distributional language representations built from large corpora have been shown to encode human-like biases, like racism and sexism (bolukbasi et al., 2016; zhao et al., 2019; lauscher et al., 1 videos demonstrating the usage of the debie application and command-line tool are available at https://tinyurl. com/y2ymujus 2020a; @xcite . at the word level, most embedding spaces, across a range of embedding models and languages @xcite , encode human biases that can be exemplified in biased analogies, such as the famous example of sexism: --→ man ---------→ programmer ≈ ----→ woman --------→ homemaker @xcite . while this is not surprising, given the distributional nature of word representation models @xcite it is -depending on the sociotechnical context -an undesired artefact of distributional representation learning @xcite which can, in turn, lead to unfair decisions in downstream applications. a number of different measures for quantifying biases in representation spaces have been proposed in recent years @xcite @xcite @xcite and even more models for removing or attenuating such biases have been developed @xcite @xcite @xcite . what is still missing, however, is the ability to seamlessly apply different bias measures and debiasing models on arbitrary embedding spaces and for custom (i.e., user-specified) bias specifications. in this work, we address this gap by introducing debie, the first integrated platform offering bias measurement and mitigation for arbitrary static embedding spaces and bias specifications. the de-bie platform is grounded in the general framework for implicit and explicit debiasing of word embedding spaces @xcite . within this framework, an implicit bias consists of measurable discrepancies between two target term sets, which can, for instance, describe a dominant and a minoritized social group (d' @xcite . in contrast, an explicit bias is a bias between such target term sets towards certain attribute terms groups. our platform allows for both implicit and explicit bias specifications, incorporating a range of different measures for quantifying embedding space bias @xcite @xcite and a pair of mutually composable methods for bias mitigation. debie's functionality for measuring and mitigating biases in distributional word vector spaces is accessible via four different interfaces: as a web application, desktop application, via a restful application programming interface (api), and as a command-line tool. we believe that debie will, by offering to test arbitrary embedding spaces for custom user-defined biases, stimulate a wider exploration of the presence of a broader set of human biases in distributional representation spaces. ## related work first, we describe related research on bias evaluation and debiasing and then turn our attention to existing bias mitigation platforms. ## debie: system description we first explain the two types of bias specifications support by debie (implicit and explicit), then proceed to describe the concrete bias specifications and debiasing algorithms bundles in the system. finally, we provide details of debie's architecture and interfaces through which the bias measuring and mitigation functionality can be accessed. all code is publicly available on github. foot_0 ## ethical considerations given the high sensitivity of the issue of bias in text representations, we would like the reader to consider the following three aspects. ## conclusion we have presented debie, an integrated platform for measuring and attenuating implicit and explicit biases in distributional word vector spaces. via four different interfaces, we enable fast and easy access to a variety of bias measures and debiasing methods, allowing users to experiment with arbitrary embedding spaces and bias specifications. we hope debie facilitates an exploration of a wider set of human biases in language representations.