  The widespread integration of Machine Learning systems in daily life,
particularly in high-stakes domains, has raised concerns about the fairness
implications. While prior works have investigated static fairness measures,
recent studies reveal that automated decision-making has long-term implications
and that off-the-shelf fairness approaches may not serve the purpose of
achieving long-term fairness. Additionally, the existence of feedback loops and
the interaction between models and the environment introduces additional
complexities that may deviate from the initial fairness goals. In this survey,
we review existing literature on long-term fairness from different perspectives
and present a taxonomy for long-term fairness studies. We highlight key
challenges and consider future research directions, analyzing both current
issues and potential further explorations.
