Recommendation systems and assistants (in short, recommenders) are ubiquitous in online platforms and influence most actions of our day-to-day lives, suggesting items or providing solutions based on users' preferences or requests. This survey analyses the impact of recommenders in four human-AI ecosystems: social media, online retail, urban mapping and generative AI ecosystems. Its scope is to systematise a fast-growing field in which terminologies employed to classify methodologies and outcomes are fragmented and unsystematic. We follow the customary steps of qualitative systematic review, gathering 144 articles from different disciplines to develop a parsimonious taxonomy of: methodologies employed (empirical, simulation, observational, controlled), outcomes observed (concentration, model collapse, diversity, echo chamber, filter bubble, inequality, polarisation, radicalisation, volume), and their level of analysis (individual, item, model, and systemic). We systematically discuss all findings of our survey substantively and methodologically, highlighting also potential avenues for future research. This survey is addressed to scholars and practitioners interested in different human-AI ecosystems, policymakers and institutional stakeholders who want to understand better the measurable outcomes of recommenders, and tech companies who wish to obtain a systematic view of the impact of their recommenders.
View on arXiv