As algorithmic decision-making (ADM) becomes prevalent in certain public sectors, its interaction with traditional bureaucratic decision-making (BDM) evolves, especially in contexts shaped by regional identities and decision-making biases. To explore these dynamics, we conducted two survey experiments within traffic enforcement scenarios, involving 4816 participants across multiple provinces. Results indicate that non-native residents perceived ADM as fairer and more acceptable than BDM when they did not share a province of origin with local bureaucrats. Both native and non-native residents showed a preference for ADM in the presence of bureaucratic and algorithmic biases but preferred BDM when such biases were absent. When bureaucratic and algorithmic biases coexisted, the lack of a shared province of origin further reinforced non-native residents' perception of ADM as fairer and more acceptable than BDM. Our findings reveal the complex interplay among province of origin, decision-making biases, and responses to different decision-making approaches.

Wang Ge, Zhang Zhejun, Xie Shenghua, Guo Yue (corresponding author). Province of Origin, Decision-Making Bias, and Responses to Bureaucratic Versus Algorithmic Decision-Making [J]. Public Administration Review, Doi: 10.1111/puar.13928.