With the in-depth development of the new generation of information technology,algorithmic decision-making (ADM) has greatly improved the scientificity and accuracy of governmentdecision-making. At the same time, algorithmic discrimination, decision-making “black box”,lack of accountability and lack of interpretability and other dilemmas brought by ADM have led the government to face new challenges of decision-making fairness. Based on the simplification theory and the theory of computer as an actor, this study constructs 2 (task objectivity: subjective task vs objective task) × 2 (emotional similarity: high vs low) two-factor inter-test experiment from the perspective of algorithmic simplification and algorithmic anthropomorphism. Taking 1356 Shanghai residents as subjects, this study analyzes the causal relationship between task objectivity and citizens?? acceptance of ADM. We also examine the moderating role of emotional similarity and the mediating role of perceived fairness in that process. This study finds that citizens have higher perceptions of procedural fairness, distribution fairness, and acceptance of ADM when performing subjective tasks. Compared with high emotional similarity, the situation with low emotional similarity further strengthens citizens?? perceptions of procedural fairness, distribution fairness and acceptance of ADM. And when the emotional similarity is low, perceived procedural fairness and distributive fairness play moderated mediating effects between the task objectivity of ADM and ADM acceptance.This study provides insights into the contextual variation of simplification properties and anthropomorphic boundaries of ADM, and puts forward suggestions on how to introduce algorithms to assist decision-making and improve algorithm design in public sector.