Perception occurs when two individuals interpret the same information differently. Despite being a known phenomenon with implications for bias in decision-making, as individuals' experience determines interpretation, perception remains largely overlooked in automated decision-making (ADM) systems. In particular, it can have considerable effects on the fairness or fair usage of an ADM system, as fairness itself is context-specific and its interpretation dependent on who is judging. In this work, we formalize perception under causal reasoning to capture the act of interpretation by an individual. We also formalize individual experience as additional causal knowledge that comes with and is used by an individual. Further, we define and discuss loaded attributes, which are attributes prone to evoke perception. Sensitive attributes, such as gender and race, are clear examples of loaded attributes. We define two kinds of causal perception, unfaithful and inconsistent, based on the causal properties of faithfulness and consistency. We illustrate our framework through a series of decision-making examples and discuss relevant fairness applications. The goal of this work is to position perception as a parameter of interest, useful for extending the standard, single interpretation ADM problem formulation.

翻译：当两个个体对相同信息产生不同理解时，便产生了感知现象。尽管这一已知现象会影响决策中的偏见（因为个体的经验决定了其理解方式），但在自动化决策（ADM）系统中，感知问题仍普遍被忽视。尤其值得注意的是，感知可能对ADM系统的公平性或公平使用产生显著影响——公平性本身具有情境依赖性，其诠释取决于评判主体的立场。本研究通过因果推理框架形式化定义了感知概念，用以捕捉个体的解释行为。我们同时将个体经验形式化为伴随个体存在且被其使用的额外因果知识。进一步地，我们定义并讨论了"负载属性"（loaded attributes）——即易于引发感知的固有属性。敏感属性（如性别、种族）即为负载属性的典型范例。基于因果的忠实性与一致性原则，我们界定了两种因果感知类型：非忠实性感知与不一致性感知。通过系列决策实例阐释该框架后，本文探讨了相关的公平性应用场景。本研究的核心目标在于将感知确立为待考察参数，为扩展标准单解释ADM问题形式化方案提供理论支撑。