Province of Origin, Decision‐Making Bias, and Responses to Bureaucratic Versus Algorithmic Decision‐Making
通过交通执法场景的两次调查实验（4816名参与者），发现非本地居民在未与本地官僚同籍贯时，认为算法决策比官僚决策更公平可接受；偏见存在时偏好算法决策，无偏见时偏好官僚决策。
ABSTRACT 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.