Research on the Application of Big Data Evidence in Criminal Procedure

Authors

  • You Wan Anhui University of Finance and Economics, Bengbu 233030, China Author

DOI:

https://doi.org/10.63313/LH.9016

Keywords:

Big data evidence, Algorithm, Application rules, Types of evidence

Abstract

The rapid development of big data technology has profoundly transformed the mode of social operation, and has also had a revolutionary impact on the field of criminal procedure. Big data evidence, with its characteristics of magnanimity, pluralism, relevance and predictability, has shown great potential in improving investigation efficiency, assisting judicial decision-making and revealing the law of crime. However, as a new form of evidence, it faces serious challenges in terms of evidence qualification, authenticity examination, relevance determina-tion, guarantee of cross-examination right and prevention of algorithm bias. This article is based on the practice of criminal proceedings in China and draws on the beneficial experience abroad to systematically explore the conceptual characteristics, application value, practical difficulties and regulatory paths of big data evidence, and puts forward countermeasures and suggestions such as improving legislation, building multi-level review rules, strengthening proce-dural rights protection and improving algorithm governance mechanisms, so as to adhere to the litigation core values of procedural justice and human rights protection while maximizing the effectiveness of big data evidence.

References

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[3] Kate Crawford, Rashida Richardson, Jason Schultz, “Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice,” New York University Law Review, Vol. 94, 2019.

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[8] Stephen E. Henderson, “A Few Criminal Justice Big Data Rules,” Ohio State Journal of Criminal Law, Vol. 15, 2017.

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Published

2025-06-26

How to Cite

Research on the Application of Big Data Evidence in Criminal Procedure. (2025). Law and Humanities, 1(2), 65–73. https://doi.org/10.63313/LH.9016