  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.
