Artificial Intelligence Empowering Doping Control: Value Implications, Practical Challenges and Development Strategies

The field of anti-doping exhibits a natural sensitivity to technological advancements, and with the rapid development of artificial intelligence(AI) technology, doping control is gradually transitioning towards intelligence-driven approaches. This study comprehensively explores the value implications, practical challenges, and development strategies of AI-enabled doping control, drawing upon literature review, logical analysis, and other research methods. The research suggests that AI-based integration and examination of anti-doping intelligence, sample testing and analysis, and intelligent management bring about transformative model changes in the doping control landscape, enabling intelligence integration and testing into one, multidimensional targeting, and comprehensive support throughout the entire chain. However, the application of AI in doping control also faces technical challenges such as model errors and unconscious bias, legitimacy concerns regarding infringements of rights and blurred responsibilities, as well as ethical risks including algorithmic discrimination, black box effects, and technological overreach. To better promote the effective utilization of AI technology in doping control, efforts should focus on data quality control, algorithm-driven transformations, and the implementation of robust technical safeguards. Additionally, the legal framework should prioritize athlete rights protection, shape regulations governing data and algorithms, and optimize oversight and accountability mechanisms. Ethical alignment can be achieved through positioning technology as an aid, prioritizing digital human rights, and fostering multistakeholder participation in governance.