浙江用 AI 抓出了贪官，准确率那是相当高

Core Viewpoint - The article discusses the innovative practice of using big data for supervision in the bidding and tendering process in Zhejiang Province, highlighting the effectiveness of the smart regulatory system in uncovering corruption and enhancing transparency in public procurement [1]. Group 1: Smart Regulatory System - The smart regulatory system for bidding supervision in Zhejiang Province is a collaborative effort led by the Provincial Commission for Discipline Inspection and the Provincial Development and Reform Commission, aimed at integrating artificial intelligence into its operations [1]. - The system has shown promising results in its initial phase, generating excitement among disciplinary inspection and supervision officials regarding its future applications [1]. Group 2: Case Study of Corruption Detection - A specific case involving the urban lighting project in Jiangshan City revealed potential corruption through data analysis conducted by the smart regulatory system, which flagged risks of unfair expert evaluations and collusion among bidders [7][9]. - The analysis indicated that despite the winning company's lack of significant technical advantages, its scores were disproportionately high, suggesting possible collusion [9]. - Following the risk alerts, local supervisory authorities conducted investigations that confirmed collusion between the winning bidder and a former official, leading to the exposure of a bribery scheme [10][13]. Group 3: Impact on Corruption Investigation - The smart regulatory system has transformed the approach to identifying corruption, providing new and accurate sources of information for disciplinary bodies, which previously relied on specific tips to investigate [13]. - The case of Feng Jiang, a former official involved in multiple bidding irregularities, illustrates how the system can uncover extensive networks of corruption once initial leads are established [15]. - The system's ability to analyze past data has allowed for the identification of corrupt practices in projects that occurred before the system's implementation, demonstrating its effectiveness in retrospective investigations [20].