Vacancy Improver: A Demo towards skill-based debiased Vacancies

Maaike H. T. de Boer, Erik M. Boertjes, Mike Wilmer, Steven Vethman, Ajaya Adhikari, Jok Tang

Abstract: Many vacancy texts do not reach their full potential; vacancies are too generic, too specific, or biased. In this demo paper, we propose a research prototype that helps users to create a better vacancy text using AI techniques in the domain of Labor Market. The proposed vacancy text from the user is analysed using an function classifier, skill extractor, bias detector and skill overlap algorithm. The Competent database consisting of functions, descriptions and skills as well as an annotated set of Dutch vacancy texts are fed to the AI techniques. In a small user evaluation, we show that the prototype has potential to help users in their need to create better vacancy texts. In future work, we aim to test the tool with more participants and improve the different functionalities.