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.

翻译：本文通过研究算法公平性与安全信息流概念之间的关系，提出了若干见解。在信息安全领域，强制实施安全信息流的问题已得到充分研究：若机密信息可能通过算法或程序“流动”，从而影响程序输出，则此类情况被视为不安全信息流，因为攻击者有可能观察（部分）机密信息。安全信息流与算法公平性之间存在强对应关系：若将种族、性别、年龄等受保护属性视为程序中的机密输入，则安全信息流意味着这些“机密”属性不能影响程序结果。尽管大多数算法公平性评估研究侧重于分析算法的影响（通常将算法视为黑箱），但从信息流推导出的概念既可用于分析差别对待，也可用于分析结构性因果模型中的差别影响。本文探讨了定量及定性信息流属性与公平性之间的关系。此外，基于这种对偶性，我们提出了一种新的定量公平性概念——公平性扩散度，该概念可通过定量信息流轻松分析，并与反事实公平性密切相关。我们证明，现成的信息流属性工具可用于形式化分析程序的算法公平性属性，包括新提出的公平性扩散度概念以及已确立的概念（如人口统计均等性）。