  Decision support systems (e.g., for ecological conservation) and autonomous
systems (e.g., adaptive controllers in smart cities) start to be deployed in
real applications. Although their operations often impact many users or
stakeholders, no fairness consideration is generally taken into account in
their design, which could lead to completely unfair outcomes for some users or
stakeholders. To tackle this issue, we advocate for the use of social welfare
functions that encode fairness and present this general novel problem in the
context of (deep) reinforcement learning, although it could possibly be
extended to other machine learning tasks.
