China Implements AI to Strengthen Anti-Corruption Efforts
China has unveiled a new strategy to tackle corruption in public procurement by leveraging artificial intelligence (AI). According to guidelines released by the National Development and Reform Commission (NDRC) along with seven other government agencies, AI systems will be used to monitor and detect irregularities in public tendering and bidding processes. This move comes as President Xi Jinping intensifies his long-standing anti-corruption campaign.
The new initiative emphasizes the importance of AI tools that can review tender documents, supervise evaluation committees, and generate actionable insights. The goal is to develop AI systems with human-like reasoning capabilities that can assist authorities in identifying and addressing possible misconduct.
AI to Detect Bid-Rigging and Provide Investigative Leads
In the official guidelines, the agencies stated that AI should be employed to uncover signs of bid-rigging and offer references for law enforcement and disciplinary bodies. These intelligent systems are expected to analyze massive datasets to spot patterns or anomalies that may indicate corruption or collusion.
The release of these guidelines follows President Xi’s January 2025 speech at the Central Commission for Discipline Inspection, China’s top anti-corruption authority. During the meeting, Xi urged for the expansion of the nation’s anti-graft measures, specifically endorsing the use of big data and AI to modernize investigative efforts.
Real-World Applications Already in Motion
China’s use of AI in anti-corruption is not merely theoretical. In Zhejiang province, authorities recently relied on AI to flag suspicious behavior in several public project tenders. As a result, a state-owned asset administrator named Feng Jiang was detained in January 2025. According to state broadcaster CCTV, AI systems identified irregularities linked to multiple projects, prompting an investigation that uncovered bribery and collusion.
Feng was accused of accepting hundreds of thousands of yuan in bribes to act as an intermediary, helping bidders influence members of tender review committees. A court sentenced him to two-and-a-half years in prison in November 2025.
“There are too many tenders and bids for us to examine each one manually,” said Wang Rongfei, a staff member at a local anti-graft agency. “Big data gives us valuable leads. Once we find a lead, we can dig deeper and build a case step by step.”
A Broader Push for Technological Oversight
China’s push to integrate AI into its governance structures reflects a growing trend to digitize public oversight. Officials believe that AI can not only enhance transparency but also provide consistent, unbiased evaluation of complex data, which is especially valuable in public procurement—an area historically vulnerable to corruption.
The guidelines suggest that AI systems should be capable of evaluating the behavior of review committee members and detecting patterns that might suggest favoritism or collusion. These capabilities are intended to improve accountability and reduce opportunities for backroom dealings.
Future Implications for Governance and Transparency
Experts suggest that if implemented effectively, China’s AI-based anti-corruption framework could serve as a model for other countries facing similar challenges. By combining big data analytics with AI’s pattern recognition abilities, governments can proactively identify and investigate misconduct.
However, the use of AI in governance also raises concerns about privacy, data integrity, and potential misuse. To ensure effectiveness and fairness, the Chinese government will need to establish strict regulations and oversight mechanisms for the deployment of these technologies.
As China continues to expand its digital governance tools, the success of these initiatives will likely depend on transparency, public trust, and the ability of AI systems to deliver accurate and actionable insights without bias.
This article is inspired by content from Original Source. It has been rephrased for originality. Images are credited to the original source.
