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Transparency in Predictive Algorithms: A Judicial Perspective
Notwithstanding the apparent hyperbole about AI promises for judicial modernization, there arise deep concerns that span from unfairness, privacy invasion, bias, discrimination to the lack of transparency and legitimacy, etc. Likewise, critics branded their application in the judicial precincts as ethically, legally, and technically distressing. Accordingly, whereas there is already an ongoing transparency debate on board, this paper attempts to revisit, extend and contribute to such simmering debate with a particular focus from a judicial perspective. Since preserving and promoting trust and confidence in the judiciary as a whole appears to be imperative, it uses a searchlight to explore how and why justice algorithms ought to be transparent as to their training data, methods, and outcomes. This paper also ends up delineating the tentative paths to do away with black-box effects and suggesting the way out for the use of algorithms in high-stake areas like the judicial settings.
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