An Information-Flow Perspective on Algorithmic Fairness
Samuel Teuber Bernhard Beckert
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.