## 6 AI and Sports Data
### AI in Sports Data: Privacy, Data Integrity, and Anti-Tampering Risks  
The application of artificial intelligence in sports data analysis is profoundly transforming the operational mode of the sports industry. However, this transformation has also brought many challenges, especially in terms of privacy and security, data integrity and tamper-proofing. First, the issue of privacy is particularly prominent. Sensitive information such as biometric data, personal performance indicators and location information generated by athletes through wearable devices is at risk of being abused or leaked. Secondly, data integrity is also a key issue that urgently needs to be addressed in sports data analysis. Sensor failures, data transmission errors, and technical bottlenecks in multi-source data fusion can all lead to the distortion of the basic data for analysis. Thirdly, the risk of malicious tampering should not be ignored either. In the highly competitive sports environment, some stakeholders may tamper with data for commercial or competitive purposes, affecting the authenticity and credibility of the analysis results. To effectively deal with the above-mentioned threats, sports organizations should establish a multi-level and systematic data security protection system. Specific measures include adopting high-strength encryption technology to ensure the security of data transmission and storage, setting up an unalterable audit trail mechanism, and using AI algorithms to identify the authenticity of data to enhance the overall data management capability. At the technical level, in 2024, [PERSON] et al. [86] proposed a privacy protection method based on enhanced Local Sensitive Hashing (LSH)--PSDPFALSH--in 2024, providing a new solution for sports data analysis. This method converts high-dimensional sensitive data into low-dimensional privacy indexes, effectively protecting data privacy while maintaining high analysis efficiency and data utilization rate. Compared with traditional hashing techniques such as MinHash and SimHash, PSDFPALSH performs better in terms of average absolute error, root mean square error and computational efficiency. This method mainly includes three stages: converting the performance data of athletes and the interaction data of fans into hash indexes that comply with privacy norms; identifying the approximate nearest neighbor relationship based on these indexes; and ultimately carrying out predictive analysis to provide personalized suggestions. This method not only optimizes the design of the LSH function, but also constructs the athlete privacy sequence through a multiple hash mechanism, effectively avoiding the risk of data leakage. At the institutional and ethical levels, [PERSON] et al. [87] report a systematic review study in 2023 that sorted out the ethical issues of AI applications in sports and summarized them into four core topics: fairness and bias, transparency and explainability, privacy and data ethics, and responsibility division. Research indicates that AI may exacerbate inequality in sports, and algorithmic black boxes also reduce the traceability and credibility of decisions. Meanwhile, the ethical use and protection of athletes' and users' data urgently needs the support of a sound system. In addition, developers and users need to clearly define their respective responsibility boundaries to promote the sustainable development of AI in sports. This study adopted the PCC framework and the PRISMA-ScR checklist for systematic retrieval and analysis, ensuring the comprehensiveness and representativeness of the research. In the future, ethical standards applicable to different sports subfields should be formulated, and a unified AI regulatory mechanism should be established globally, with particular attention paid to the application of amateur sports, the improvement of dataset diversity, and the integration of ethical AI in the sports governance structure.