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Artificial intelligence (AI) in Bangladesh's garment industry: Addressing legal gaps and advancing regulatory readiness

A. Srivastava et al.

Common Law World Review2026https://doi.org/10.1177/14737795261423557article
ABDC B
Weight
0.50

Abstract

The rapid advancement of artificial intelligence (AI) is transforming global industries, yet its adoption in developing economies raises urgent questions about fairness, transparency, accountability, and legal oversight. This multidisciplinary empirical study examines the regulatory challenges of AI adoption in Bangladesh's Ready-Made Garment (RMG) sector, a labour-intensive industry central to national economic growth. Drawing on stakeholder insights, the research highlights key concerns relating to fairness and transparency, accountability and liability, privacy and data protection, risk regulation, and the critical need for education and awareness. The findings expose significant gaps in sector-specific governance and institutional readiness. In response, the study proposes a seven-step process to guide the development of effective and context-sensitive regulatory frameworks. While focused on Bangladesh, the recommendations offer broader relevance to other common law countries such as India and Malaysia. This article contributes to building a more inclusive, ethical, and enforceable foundation for AI governance in emerging economies.

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https://doi.org/https://doi.org/10.1177/14737795261423557

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@article{a.2026,
  title        = {{Artificial intelligence (AI) in Bangladesh's garment industry: Addressing legal gaps and advancing regulatory readiness}},
  author       = {A. Srivastava et al.},
  journal      = {Common Law World Review},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/14737795261423557},
}

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Artificial intelligence (AI) in Bangladesh's garment industry: Addressing legal gaps and advancing regulatory readiness

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.