The field of Artificial Intelligence (AI) is focusing on creating automated decision-making (ADM) systems that operate as close as possible to human-like intelligence. This effort has pushed AI researchers into exploring cognitive fields like psychology. The work of Daniel Kahneman and the late Amos Tversky on biased human decision-making, including the study of the conjunction fallacy, has experienced a second revival because of this. Under the conjunction fallacy a human decision-maker will go against basic probability laws and rank as more likely a conjunction over one of its parts. It has been proven overtime through a set of experiments with the Linda Problem being the most famous one. Although this interdisciplinary effort is welcomed, we fear that AI researchers ignore the driving force behind the conjunction fallacy as captured by the Linda Problem: the fact that Linda must be stereotypically described as a woman. In this paper we revisit the Linda Problem and formulate it as a fairness problem. In doing so we introduce perception as a parameter of interest through the structural causal perception framework. Using an illustrative decision-making example, we showcase the proposed conceptual framework and its potential impact for developing fair ADM systems.

翻译：摘要：人工智能领域正致力于创建尽可能接近人类智能的自动化决策系统。这一努力促使人工智能研究者探索心理学等认知领域。丹尼尔·卡尼曼与已故的阿莫斯·特沃斯基关于人类决策偏差的研究（包括对合取谬误的探讨）因此迎来了第二次复兴。在合取谬误中，人类决策者会违背基本概率法则，认为合取事件的概率高于其组成部分的概率。该现象已通过一系列实验被反复验证，其中最著名的便是琳达问题。尽管这类跨学科研究值得欢迎，但我们担忧人工智能研究者忽视了琳达问题所揭示的合取谬误背后的驱动力：琳达必须被刻板地描述为女性这一事实。本文重新审视琳达问题，并将其重构为一个公平性问题。在此过程中，我们通过结构化因果感知框架引入感知作为感兴趣参数。通过一个示例性决策案例，我们展示了所提出的概念框架及其对开发公平自动化决策系统的潜在影响。