  It is known that recommendations of AI-based systems can be incorrect or
unfair. Hence, it is often proposed that a human be the final decision-maker.
Prior work has argued that explanations are an essential pathway to help human
decision-makers enhance decision quality and mitigate bias, i.e., facilitate
human-AI complementarity. For these benefits to materialize, explanations
should enable humans to appropriately rely on AI recommendations and override
the algorithmic recommendation when necessary to increase distributive fairness
of decisions. The literature, however, does not provide conclusive empirical
evidence as to whether explanations enable such complementarity in practice. In
this work, we (a) provide a conceptual framework to articulate the
relationships between explanations, fairness perceptions, reliance, and
distributive fairness, (b) apply it to understand (seemingly) contradictory
research findings at the intersection of explanations and fairness, and (c)
derive cohesive implications for the formulation of research questions and the
design of experiments.
