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The field of Artificial Intelligence (AI) is focusing on creating automateddecision-making (ADM) systems that operate as close as possible to human-likeintelligence. This effort has pushed AI researchers into exploring cognitivefields like psychology. The work of Daniel Kahneman and the late Amos Tverskyon biased human decision-making, including the study of the conjunctionfallacy, has experienced a second revival because of this. Under theconjunction fallacy a human decision-maker will go against basic probabilitylaws and rank as more likely a conjunction over one of its parts. It has beenproven overtime through a set of experiments with the Linda Problem being themost famous one. Although this interdisciplinary effort is welcomed, we fearthat AI researchers ignore the driving force behind the conjunction fallacy ascaptured by the Linda Problem: the fact that Linda must be stereotypicallydescribed as a woman. In this paper we revisit the Linda Problem and formulateit as a fairness problem. In doing so we introduce perception as a parameter ofinterest through the structural causal perception framework. Using anillustrative decision-making example, we showcase the proposed conceptualframework and its potential impact for developing fair ADM systems.

What's the Problem, Linda? The Conjunction Fallacy as a Fairness Problem