  Fairness is central to the ethical and responsible development and use of AI
systems, with a large number of frameworks and formal notions of algorithmic
fairness being available. However, many of the fairness solutions proposed
revolve around technical considerations and not the needs of and consequences
for the most impacted communities. We therefore want to take the focus away
from definitions and allow for the inclusion of societal and relational aspects
to represent how the effects of AI systems impact and are experienced by
individuals and social groups. In this paper, we do this by means of proposing
the ACROCPoLis framework to represent allocation processes with a modeling
emphasis on fairness aspects. The framework provides a shared vocabulary in
which the factors relevant to fairness assessments for different situations and
procedures are made explicit, as well as their interrelationships. This enables
us to compare analogous situations, to highlight the differences in dissimilar
situations, and to capture differing interpretations of the same situation by
different stakeholders.
