Fairness in vulnerable attribute prediction on social media

Historically, policymakers and practitioners relied exclusively on survey and census data to design and plan for assistive interventions; now, social media offer a timely and cost-effective way to reach out to populations otherwise unobserved. This study was designed to address the needs of a non-for-profit organisation to reach out to the young unemployed individuals in Italy with educational and job opportunities via communication channels that are more likely to appeal to younger generations. To this extend, we developed an ad-hoc Facebook application which administers questionnaires while gathering data about the Likes on Facebook Pages. Then, we developed a machine learning framework that successfully predicts the unemployment status of an unseen individual (.74 AUC). However, blindly delegating to the machine learning model the communication intervention may lead to digital discrimination on the basis of socio-demographic characteristics. Here, we propose a framework that aims to optimising both for the prediction performance as well as the most adequate fairness metric. Our framework is based on an adaptive threshold for gender, while we show that it can be expanded for other socio-demographic attributes and generalised for other interventions of assistive character. We present a doubly cross-validated setting that achieves out-of-sample stability and generalisability of results. We compare the behaviour of models that infer on different sets of data and provide an indepth discussion on the most predictive features, demonstrating that the “fairness through unawareness” approach does not suffice to achieve a fair classification since sensitive demographic information can be inferred not only via other sociodemographic attributes but also from behavioural digital patterns. Finally, we thoroughly assess the behaviour of the adaptive threshold approach and provide an in-depth discussion on the advantages but also the implications of such models offering actionable insights. Our results show that careful assessment of fairness metrics should be considered, primarily when AI models are employed for policymaking.