Abstract:

To address the social security risks arising from biases in the underlying algorithms of intelligent applications, this study employs literature review and case analysis methods to systematically retrieve key publications in algorithm ethics and digital governance from recent years both domestically and internationally. By examining major social incidents with significant impact, it first analyzes the risk transmission chain of algorithmic bias. Second, it identifies five fundamental technical biases: historical bias, representation/representativeness bias, measurement/labeling bias, aggregation/algorithmic design bias, and evaluation/deployment bias. Third, it examines the causes of algorithmic bias across four dimensions—technical, commercial, human, and institutional—to further pinpoint five potential types of social security risks. Finally, it proposes the establishment of a multi-faceted governance system for algorithmic bias, comprising“technical correction—institutional constraints—social co-governance”.

Key words: intelligent application, algorithmic bias, algorithmic ethics, social security, digital governance