  Recommender systems are the algorithms which select, filter, and personalize
content across many of the worlds largest platforms and apps. As such, their
positive and negative effects on individuals and on societies have been
extensively theorized and studied. Our overarching question is how to ensure
that recommender systems enact the values of the individuals and societies that
they serve. Addressing this question in a principled fashion requires technical
knowledge of recommender design and operation, and also critically depends on
insights from diverse fields including social science, ethics, economics,
psychology, policy and law. This paper is a multidisciplinary effort to
synthesize theory and practice from different perspectives, with the goal of
providing a shared language, articulating current design approaches, and
identifying open problems. It is not a comprehensive survey of this large
space, but a set of highlights identified by our diverse author cohort. We
collect a set of values that seem most relevant to recommender systems
operating across different domains, then examine them from the perspectives of
current industry practice, measurement, product design, and policy approaches.
Important open problems include multi-stakeholder processes for defining values
and resolving trade-offs, better values-driven measurements, recommender
controls that people use, non-behavioral algorithmic feedback, optimization for
long-term outcomes, causal inference of recommender effects, academic-industry
research collaborations, and interdisciplinary policy-making.
