WHEN ELIMINATING BIAS ISN’T FAIR: ALGORITHMS, QUANTIFICATION, AND PROCEDURAL JUSTICE
Speaker
Nate FastLocation
Phelps 1410, Executive Learning CenterInfo
The perceived fairness of decision-making procedures is a key concern for organizations, particularly when evaluating employees and determining personnel outcomes. Algorithms have created opportunities for increasing fairness and impartiality by overcoming biases commonly displayed by human decision makers. However, while existing theory generally supports this line of reasoning, we argue that it overlooks the possibility that algorithmic decision making violates the norms of procedural justice by appearing to reduce employees to quantifiable attributes, stripping away the qualitative aspects of their human nature. Results from two laboratory experiments (N = 388) and a large-scale randomized experiment in an organizational setting (N = 1,654) provide evidence for this theory by demonstrating that people view decisions determined by algorithms as less fair than identical decisions made by human evaluators. Moreover, this perceived unfairness significantly reduced reported organizational commitment. Theoretical and practical implications for organizations using algorithms and data analytics more broadly are discussed.
