  Ensuring fairness in computational problems has emerged as a $key$ topic
during recent years, buoyed by considerations for equitable resource
distributions and social justice. It $is$ possible to incorporate fairness in
computational problems from several perspectives, such as using optimization,
game-theoretic or machine learning frameworks. In this paper we address the
problem of incorporation of fairness from a $combinatorial$ $optimization$
perspective. We formulate a combinatorial optimization framework, suitable for
analysis by researchers in approximation algorithms and related areas, that
incorporates fairness in maximum coverage problems as an interplay between
$two$ conflicting objectives. Fairness is imposed in coverage by using coloring
constraints that $minimizes$ the discrepancies between number of elements of
different colors covered by selected sets; this is in contrast to the usual
discrepancy minimization problems studied extensively in the literature where
(usually two) colors are $not$ given $a$ $priori$ but need to be selected to
minimize the maximum color discrepancy of $each$ individual set. Our main
results are a set of randomized and deterministic approximation algorithms that
attempts to $simultaneously$ approximate both fairness and coverage in this
framework.
