Publications

Journal Articles

Embracive, pervasive, and unstoppable global algorithmization greatly influences the deployment of artificial intelligence systems in criminal courts to replace obsolete bail and sentencing practices, reduce recidivism risk, and modernize judicial practices. Since artificial intelligence systems have provably appeared to have the duality of golden promises and potential perils, applying such a system in the justice system also entails some associated risks. Hence, allocating this unchecked novel resource in judicial domains sparks vigorous debate over its legal and ethical implications. With such backgrounds, this paper examines how and why artificial intelligence systems reinforce bias and discrimination in society and suggests what approach could be an alternative to the current predictive justice mechanisms in use.

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This paper examines the doctrine of Basic Structure and its effect on developing constitutional jurisprudence in the Indian sub-continent, i.e. Bangladesh, India, and Pakistan. It also analyses the courts' robust underpinning on the doctrine that puts 'an embargo or a limit upon the parliamentary supremacy'. Such an embargo or a limit is seen as a fundamental one that eventually leads to practical application. Since this position provokes huge controversy, e.g. illegitimacy, anti-democracy, judiciocracy, counter-majoritarianism, anathema to popular sovereignty, non-textual and abstract formulation, etc., it fairly requires doctrinal expositions to be made on how to figure it out. Moreover, it explores the scope, standing, and inviolability of the doctrine in the sense of comparative conceptualization. Furthermore, dissection from the multifarious perspective is made to look into its pragmatism, justification, and legitimacy through fixing today's position in the ongoing debate among contemporary constitutional commentators. 

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With the data landscape of the universe expanding every second every day by leaps and bounds, 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, cyber threats, etc. Taking effective efforts to mitigate 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 created in the face of Big Data.

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Notwithstanding the apparent hyperbole about AI promises for judicial modernization, there arise deep concerns that span from unfairness, privacy invasion, bias, and 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 a 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 judicial settings.

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Journal Articles