  Machine Learning (ML) decision-making algorithms are now widely used in
predictive decision-making, for example, to determine who to admit and give a
loan. Their wide usage and consequential effects on individuals led the ML
community to question and raise concerns on how the algorithms differently
affect different people and communities. In this paper, we study fairness
issues that arise when decision-makers use models (proxy models) that deviate
from the models that depict the physical and social environment in which the
decisions are situated (intended models). We also highlight the effect of
obstacles on individual access and utilization of the models. To this end, we
formulate an Equity Framework that considers equal access to the model, equal
outcomes from the model, and equal utilization of the model, and
consequentially achieves equity and higher social welfare than current fairness
notions that aim for equality. We show how the three main aspects of the
framework are connected and provide an equity scoring algorithm and questions
to guide decision-makers towards equitable decision-making. We show how failure
to consider access, outcome, and utilization would exacerbate proxy gaps
leading to an infinite inequity loop that reinforces structural inequities
through inaccurate and incomplete ground truth curation. We, therefore,
recommend a more critical look at the model design and its effect on equity and
a shift towards equity achieving predictive decision-making models.
