Cognitive Biases and Decision Noise in Hiring Decisions: A Literature Review

Abstract

Hiring decisions determine who enters an organization, yet the process by which recruiters and managers evaluate candidates is rarely as objective as organizations intend it to be. This literature review synthesizes research on the cognitive biases and decision noise that distort hiring judgments, drawing on foundational work in judgment and decision-making (Tversky & Kahneman, 1974; Kahneman, Sibony, & Sunstein, 2021) as well as more recent empirical and organizational research. The review defines cognitive bias and decision noise, surveys the most consequential biases identified in the selection literature—including the halo and horn effects, confirmation bias, similarity or affinity bias, anchoring, availability, representativeness, stereotyping, and status-quo bias—and examines the theoretical frameworks used to explain them, namely Dual Process Theory, the Heuristics and Biases framework, Bounded Rationality, and Noise theory. It then considers empirical evidence of bias in real hiring contexts, including field experiments on racial discrimination in callbacks (Bertrand & Mullainathan, 2004) and cultural matching in elite firms (Rivera, 2012), before turning to the promises and risks of algorithmic hiring tools. The review concludes by summarizing evidence-based mitigation strategies—structured interviews, decision hygiene, blind screening, and governed use of artificial intelligence—and argues that addressing both bias and noise is essential to fair and effective personnel selection.