This paper looks at an algorithmically- led decision process that is designed to select an (almost) perfectly demographically representative cross-section for a Jury Venire using Big Data.
With the scope of Lepri et al. (2017), this paper identifies that "dark sides" such as privacy violations, informational opacity and discrimination are likely to apply to such a (yet) hypothetical Big Data Jury Venire selection process. Answering the question of how this selection process could be positively disrupted is posed, this paper finds that policies akin to Lepri et al. (2017) would address the majority of the problems identified.
Further research will be required to illuminate further potential dark-sides, define more general, positively disruptive policies, as well as to specify policy suggestions.
Table of Contents
1 Introduction
2 Key Concepts
2.1 Big Data
2.2 Algorithm
2.3 Positive Disruption
3 Big Data Jury Venire Selection
3.1 Context and Outline of Hypothetical Jury Venire Selection Algorithm
3.2 Social Good – The “Bright Side”
3.3 A “Dark Side” Diagnosis of Algorithmic Big Data Jury Venire Selection
3.3.1 Computational Violations of Privacy
3.3.2 Information Asymmetry and Lack of Information
3.3.3 Social Exclusion and Discrimination
3.4 Prescription for Positive Disruption
3.5 Evaluation and Summary
4 Conclusion
Bibliography
Appendix
Appendix A
- Quote paper
- Maike Heideke (Author), 2019, The Positive Disruption of Big Data Jury Venire Selection, Munich, GRIN Verlag, https://www.grin.com/document/499629
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