Recommendation Fairness in eParticipation: Listening to Minority, Vulnerable and NIMBY Citizens

E-participation refers to the use of digital technologies and online platforms to engage citizens and other stakeholders in democratic and government decision-making processes. Recent research work has explored the application of recommender systems to e-participation, focusing on the development of algorithmic solutions to be effective in terms of personalized content retrieval accuracy, but ignoring underlying societal issues, such as biases, fairness, privacy and transparency. Motivated by this research gap, on a public e-participatory budgeting dataset, we measure and analyze recommendation fairness metrics oriented to several minority, vulnerable and NIMBY (Not In My Back Yard) groups of citizens. Our empirical results show that there is a strong popularity bias (especially for the minority groups) due to how content is presented and accessed in a reference e-participation platform; and that hybrid algorithms exploiting user geolocation information in a collaborative filtering fashion are good candidates to satisfy the proposed fairness conceptualization for the above underrepresented citizen collectives.