This work presents results on the use of Information-Flow tools for the formal verification of algorithmic fairness properties. The problem of enforcing secure information-flow was originally studied in the context of information security: If secret information may “flow” through an algorithm in such a way that it can influence the program’s output, we consider that to be insecure information-flow as attackers could potentially observe (parts of) the secret. Due to its wide-spread use, there exist numerous tools for analyzing secure information-flow properties. Recent work showed that there exists a strong correspondence between secure information-flow and algorithmic fairness: If protected group attributes are treated as secret program inputs, then secure information-flow means that these “secret” attributes cannot influence the result of a program. We demonstrate that off-the-shelf tools for information-flow can be used to formally analyze algorithmic fairness properties including established notions such as (conditional) demographic parity as well as a new quantitative notion named fairness spread.