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ABSTRACT
The explosive growth of information and the development of data processing technology have generated ‘big data’ tools, which have opened up major transformations in various fields. The field of criminal justice has also been subjected to big data application without exception, and it is undergoing a major development from partial data statistics to ‘full data’ analysis, from retrospective thinking to predictive thinking. Big data has been applied to various stages of investigation, prosecution, and delivery of punishment, but it still faces the issues of fairness and legitimacy, and its utility cannot be truly realized. Effective regulation and supervision of data and algorithms can prevent discrimination and improve the fairness of big data tools, and by redesigning relevant judicial procedure rules and clarifying the basic principles of personal information data collection, it can help realize big data applications and secure basic rights and achieve the best balance between protection which make it acceptable to the public. Finally, the auxiliary role of big data in criminal justice needs to be reassured which means it cannot and should not overstep the tasks that are exclusively for criminal justice decision-makers.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
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2 See Viktor Mayer-Schönberger and Kenneth Cukier, Big Data: A Revolution That Will Transform How We Live, Work, and Think, (Sheng Yangyan and Zhoutao trs, Zhejiang People's Publishing House 2013) 27–44.
3 See Feng Liqiang, ‘A Comparative Study of the Atomic Model and the Whole Model of Fact Finding’17(00) (2020) Evidence Forum 106.
4 See Viktor (n 2) 67.
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10 See Jin Jiangju and Guo Yinglou, Smart City: Big Data, City Governance in the Era of Internet (4th edn, China Industry and Information Technology Publishing 2016) 32.
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14 M Hamilton, ‘Risk-needs Assessment: Constitutional and Ethical Challenges’ (2015) 52 University of Houston Law Center 231-291.
15 See D Kehl, P Guo and S Kessler, ‘Algorithms in the Criminal Justice System: Assessing the Use of Risk Assessments in Sentencing’ (2017) Responsive Communities Initiative, Berkman Klein Center For Internet & Society, Harvard Law School <http://nrs.harvard.edu/urn-3:HUL.InstRepos:33746041> accessed 20 May 2020.
16 See J Angwin and others, ‘Machine Bias: There’s Software Used Across the Country to Predict Future Criminals. And It’s Aiased Against Blacks’ (2016) Propublica <www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing> accessed 20 May 2020; J Dressel and H Farid, ‘The Accuracy, Fairness, and Limits of Predicting Recidivism’ (2018) 4 Science Advances 1.
17 See H C Kraemer and others, ‘Coming to Terms with the Terms of Risk’ (1997) 54 Archives of General Psychiatry 337.
18 See C W Lidz, E P Mulvey and W Gardner, ‘The Accuracy of Predictions of Violence to Others’ (1993) 269 JAMA 1007.
19 See J L Johnson and others, ‘The Construction and Validation of the Federal Post Conviction Risk Assessment (PCRA)’ (2011)75 Federal Probation 16.
20 See C O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown Publishers 2016).; S B Starr, ‘Evidence-based Sentencing and the Scientific Rationalization of Discrimination’ (2014) 66 Stanford Law Review 803; M Tonry, ‘Legal and Ethical Issues in the Prediction of Recidivism’ (2014) 26 Federal Sentencing Reporter 167.
21 See Wang Ran, ‘Research on the Transformation of Investigation Mode in the Big Data Era and Its Legal Issue’ (2018) 24 (05) Law and Social Development 110.
22 See Cheng Lei, ‘Legal Control of Big Data Investigation’ (2019) 03 Social Sciences of China:English Version 189.
23 See S B Starr, ‘Evidence-based Sentencing and the Scientific Rationalization of Discrimination’ (2014) 66 Stanford Law Review 803.
24 See A Zavrnik, ‘Algorithmic Justice: Algorithms and Big Data in Criminal Justice Settings’ (2021) 18 (5) European Journal of Criminology 623.
25 See D Ensign and others, ‘Runaway Feedback Loops in Predictive Policing’ (2018) 81 Proceedings of Machine Learning Research 1; K Lum and W Isaac, ‘To Predict and Serve?’ (2016) 13 Significance 14; A D Selbst, ‘Disparate Impact in Big Data Policing’ (2018) Georgia Law Review 109.
26 See R Simmons, ‘Quantifying Criminal Procedure: How to Unlock the Potential of Big Data in Our Criminal Justice System’ (2016) 4 Michigan State Law Review 947; A G Ferguson, The Rise of Big Data Policing: Surveillance, Race, and the Future of Law Enforcement (NYU Press 2017).
27 See R Simmons. Big data, ‘Machine Judges, and the Legitimacy of the Criminal Justice System’ (2018) SSRN Electronic Journal 1067.
28 See E E Joh, ‘Policing by Numbers: Big Data and the Fourth Amendmente’ (2014) 89 Washington Law Review 35; A H Loewy, ‘Rethinking Search and Seizure in a Post-911 World’ (2011) 90 Mississippi Law Journal 1507.
29 See Cheng (n 22).
30 See Pei Wei, ‘The procedural law regulation of data investigation – Based on the investigation of the correlation of investigation behavior’ (2019) 37 (06) Science of Law (Journal of Northwest of Politiceal Science of Law 43.
31 See Simmons (n 27).
32 See A Memmi, The Colonizer and the Colonize (ORION Press 1965).
33 See Simmons (n 27).
34 See Wang (n 21).
35 See Pei (n 30).
36 See Wang (n 21).
37 See Pei (n 30).
38 See Wang (n 21).
39 See E Edwards (2016), ‘Predictive Policing Software is More Accurate at Predicting Policing than Predicting crime’ (HUFFINGTON POST, 31 Auguest 2020) <https://www.huffpost.com/entry/predictive-policing-reform_b_57c6ffe0e4b0e60d31dc9120> accessed May 20 2020; Ferguson(n 1); A Maki, Crimes Lurk in Police Mems. Communication Appeal (Memphis), JAN. 25 (2012).
40 See Ferguson (n 1).
41 See Wang (n 21).
42 See Cheng (n 22).
43 See Simmons (n 27).
44 See A A Braga, ‘The Effects of Hot Spots Policing on Crime’ (2001) 578 The Annals of the American Academy of Political and Social Science 104.
45 See Cheng (n 22).
46 See Ridgeway, & Greg., ‘Policing in the Era of Big Data’ (2018)1(1) ANNUAL REVIEW OF CRIMINOLOGY 401.
47 See Wang (n 21).
48 ibid.
49 See Cheng (n22).
50 See Zavrnik (n 24).
51 See Simmons (n 27).
52 See Jiang Su, ‘Automated Decision Making, Criminal Justice, and Algorithmic Regulation’ (2020) Oriental Law 3.
53 See Pei (n 30).
54 ibid.
55 See Jiang (n 52).
56 See Wang (n 21).
57 See Cheng (n 22).
58 See Pei (n 30).
59 See Cheng (n 22).
60 See Wang (n 21).
61 See Pei (n 30).
62 See Zhang Linghan, Liang Yuhan, ‘The Application, Dilemma and Enlightenment of Algorithms in Extraterritorial Judicial Practice’ (China Trail, 21 April 2020) <https://mp.weixin.qq.com/s/1Vzh0sAjSxp1fSDpYlOOiw> accessed May 20 2020.
63 See Jiang (n 52)
64 See Pei (n 30)