An implicit ambiguity in the field of prediction-based decision-making regards the relation between the concepts of prediction and decision. Much of the literature in the field tends to blur the boundaries between the two concepts and often simply speaks of 'fair prediction.' In this paper, we point out that a differentiation of these concepts is helpful when implementing algorithmic fairness. Even if fairness properties are related to the features of the used prediction model, what is more properly called 'fair' or 'unfair' is a decision system, not a prediction model. This is because fairness is about the consequences on human lives, created by a decision, not by a prediction. We clarify the distinction between the concepts of prediction and decision and show the different ways in which these two elements influence the final fairness properties of a prediction-based decision system. In addition to exploring this relationship conceptually and practically, we propose a framework that enables a better understanding and reasoning of the conceptual logic of creating fairness in prediction-based decision-making. In our framework, we specify different roles, namely the 'prediction-modeler' and the 'decision-maker,' and the information required from each of them for being able to implement fairness of the system. Our framework allows for deriving distinct responsibilities for both roles and discussing some insights related to ethical and legal requirements. Our contribution is twofold. First, we shift the focus from abstract algorithmic fairness to context-dependent decision-making, recognizing diverse actors with unique objectives and independent actions. Second, we provide a conceptual framework that can help structure prediction-based decision problems with respect to fairness issues, identify responsibilities, and implement fairness governance mechanisms in real-world scenarios.

翻译：预测导向型决策领域中存在一个隐含的模糊性，即预测与决策这两个概念之间的关系。该领域的大量文献往往模糊了两者之间的界限，并常以"公平预测"一言蔽之。本文指出，在实施算法公平性时，区分这两个概念具有重要意义。即使公平属性与所使用预测模型的特征相关，但更恰当地被称为"公平"或"不公平"的是决策系统，而非预测模型。这是因为公平性关乎决策对人类生活产生的影响，而非预测本身。我们澄清了预测与决策概念之间的区别，并展示了这两个要素影响预测导向型决策系统最终公平属性的不同方式。除了从概念和实践角度探讨这一关系外，我们提出了一个框架，能够增进对预测导向型决策中实现公平性的概念逻辑的理解与推理。在该框架中，我们明确了不同角色——即"预测建模者"与"决策者"——以及各自为实施系统公平性所需的信息。我们的框架可推导出这两个角色的明确责任，并讨论与伦理及法律要求相关的一些见解。我们的贡献包含两点：首先，将关注点从抽象的算法公平性转向情境依赖的决策过程，认识到具有独特目标和独立行动的不同行动者；其次，提供了一个概念框架，有助于围绕公平性问题构建预测导向型决策问题、明确责任，并在现实场景中实施公平治理机制。