  A growing literature on human-AI decision-making investigates strategies for
combining human judgment with statistical models to improve decision-making.
Research in this area often evaluates proposed improvements to models,
interfaces, or workflows by demonstrating improved predictive performance on
"ground truth" labels. However, this practice overlooks a key difference
between human judgments and model predictions. Whereas humans reason about
broader phenomena of interest in a decision -- including latent constructs that
are not directly observable, such as disease status, the "toxicity" of online
comments, or future "job performance" -- predictive models target proxy labels
that are readily available in existing datasets. Predictive models' reliance on
simplistic proxies makes them vulnerable to various sources of statistical
bias. In this paper, we identify five sources of target variable bias that can
impact the validity of proxy labels in human-AI decision-making tasks. We
develop a causal framework to disentangle the relationship between each bias
and clarify which are of concern in specific human-AI decision-making tasks. We
demonstrate how our framework can be used to articulate implicit assumptions
made in prior modeling work, and we recommend evaluation strategies for
verifying whether these assumptions hold in practice. We then leverage our
framework to re-examine the designs of prior human subjects experiments that
investigate human-AI decision-making, finding that only a small fraction of
studies examine factors related to target variable bias. We conclude by
discussing opportunities to better address target variable bias in future
research.
