  Perception occurs when two individuals interpret the same information
differently. Despite being a known phenomenon with implications for bias in
decision-making, as individual experience determines interpretation, perception
remains largely overlooked in machine learning (ML) research. Modern decision
flows, whether partially or fully automated, involve human experts interacting
with ML applications. How might we then, e.g., account for two experts that
interpret differently a deferred instance or an explanation from a ML model? To
account for perception, we first need to formulate it. In this work, we define
perception under causal reasoning using structural causal models (SCM). Our
framework formalizes individual experience as additional causal knowledge that
comes with and is used by a human expert (read, decision maker). We present two
kinds of causal perception, unfaithful and inconsistent, based on the SCM
properties of faithfulness and consistency. Further, we motivate the importance
of perception within fairness problems. We illustrate our framework through a
series of decision flow examples involving ML applications and human experts.
