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Industry Case Studies on Building and Governing Trustworthy AI Platforms

Deepak GuptaInstitute of Technology and Management, Gwalior, IndiaErgashev Nuriddin GayratovichKarshi State Technical University, Karshi, UzbekistanAsadbek KuzievMamun University, Khorezm, UzbekistanNozim Muminov GaffarovichAlfraganus University, Tashkent, UzbekistanKarlibaeva Gulshat KhojabaevnaTashkent State University of Economics, Tashkent, UzbekistanNasiba EshchanovaGafurov AnvarAlfraganus University, Tashkent, Uzbekistan

Advances in computational intelligence and robotics book seriesbook series2026

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Annotatsiya

As artificial intelligence systems proliferate across critical industry sectors, the imperative to build platforms that are simultaneously capable and trustworthy has grown urgent. This chapter examines five cross-sector case studies—spanning financial services, healthcare, manufacturing, public administration, and retail—to surface the governance architectures, transparency mechanisms, and accountability structures that distinguish trustworthy AI deployments from opaque, high-risk alternatives. Drawing on responsible AI governance frameworks, the Digital Trust Ecosystem Framework (DTEF), NIST AI RMF, and the EU AI Act, the chapter synthesises recurring patterns: layered data governance, explainability-by-design, fairness auditing pipelines, and human oversight protocols.

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Mavzular

Ethics and Social Impacts of AI Big Data and Business Intelligence Robotic Process Automation Applications

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DOI: 10.4018/979-8-3373-6761-3.ch008

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