  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.
