Privacy and Customer's Education: NLP for Information Resources Suggestions and Expert Finder Systems

Luca Mazzola, Andreas Waldis, Atreya Shankar, Diamantis Argyris, Alexander Denzler, Michiel Van Roey

Abstract: Privacy is one of the key issues for citizen’s everyday online activities, with the United Nations defining it as “a human right in the digital age”. Despite the introduction of data privacy regulations almost everywhere around the globe, the biggest barrier to effectiveness is the customer’s capacity to map the privacy statement received with the regulation in force and understand their terms. This study advocates the creation of a convenient and cost-efficient question-answering service for answering customers’ queries on data privacy. It proposes a dual step approach, allowing consumers to ask support to a conversational agent boosted by a smart knowledge base, attempting to answer the question using the most appropriate legal document. Being the self-help approach insufficient, our system enacts a second step suggesting a ranked list of legal experts for focused advice. To achieve our objective, we need large enough and specialised dataset and we plan to apply state-of-the-art Natural Language Processing (NLP) techniques in the field of open domain question answering. This paper describes the initial steps and some early results we achieved in this direction and the next steps we propose to develop a one-stop solution for consumers privacy needs.