  This work presents insights gained by investigating the relationship between
algorithmic fairness and the concept of secure information flow. The problem of
enforcing secure information flow is well-studied in the context of information
security: If secret information may "flow" through an algorithm or program in
such a way that it can influence the program's output, then that is considered
insecure information flow as attackers could potentially observe (parts of) the
secret.
  There is a strong correspondence between secure information flow and
algorithmic fairness: if protected attributes such as race, gender, or age are
treated as secret program inputs, then secure information flow means that these
``secret'' attributes cannot influence the result of a program. While most
research in algorithmic fairness evaluation concentrates on studying the impact
of algorithms (often treating the algorithm as a black-box), the concepts
derived from information flow can be used both for the analysis of disparate
treatment as well as disparate impact w.r.t. a structural causal model.
  In this paper, we examine the relationship between quantitative as well as
qualitative information-flow properties and fairness. Moreover, based on this
duality, we derive a new quantitative notion of fairness called fairness
spread, which can be easily analyzed using quantitative information flow and
which strongly relates to counterfactual fairness. We demonstrate that
off-the-shelf tools for information-flow properties can be used in order to
formally analyze a program's algorithmic fairness properties, including the new
notion of fairness spread as well as established notions such as demographic
parity.
