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.

With the data landscape of the universe expands every second every day by leaps and bound, the data value also increases unprecedentedly. Particularly, the disruptive use of data in location tracking, predictive policing, fraud detection, healthcare, advertising media, and entertainment has already revitalized personal data in many ways. But massive amassing of data also gives rise to new issues regarding the Big Data effects, including privacy invasion, data breaches, and cyber threats, etc. Taking effective efforts for mitigating the risks of data explosion thus becomes indispensable for companies, organizations, and societies alike. In such background, this paper attempts to focus on the ways how the data minimization approach mitigates such risks, and how this approach as a concept is being incorporated in legal instruments globally. After exploring practical methods of applying data minimization, the paper concludes by delineating the way out of the existing dilemmas so created in the face of Big Data.