Maqola

Responsible AI and Scalable Data Architectures: Challenges and Best Practices

Shilesh KarunakaranUniversity of Cincinnati, Carl H. Lindner College of Business,Cincinnati,OH,USALaxmi VanamMissouri University of Science and TechnologyGayatri TavvaRajeev Gandhi Memorial College of Engineering and Technology,Nandyala,Andhra Pradesh,IndiaSardor SabirovR R RajabovUrgench State University,Department of Administration,Urgench,Uzbekistan

2025

ABI

Annotatsiya

The proliferation of AI across industries — in finance and health care and retail, and logistics — has made it imperative to have systems that are not just scalable and efficient, but also ethical and accountable. In this paper, we review the intersection between Responsible AI (RAI) and scalable data architectures, discussing seminal research contributions, software coding approaches, and empirical benchmarks. We present a multi-layered ethical AI stack and show how fairness, transparency, privacy, and governance can be implemented in distributed, high-throughput settings. Finally, the review provides prospective views into emerging technologies, regulatory landscapes and interdisciplinary interactions that will change how AI systems will be designed and built to ensure trust and resilience in the future.

Hali tarjima qilinmagan

Mavzular

Ethics and Social Impacts of AI Adversarial Robustness in Machine Learning Privacy-Preserving Technologies in Data

Identifikatorlar

DOI: 10.1109/icsit65336.2025.11295475

Iqtiboslar va manbalar

0 ta iqtibos13 ta foydalanilgan manba

Koʻrsatkichlar — AkademScholar