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