  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.
