ISCSR Research Publishing
Journal of Algorithmic Discovery and Applied AI
Review Article Published October 3, 2026 Article jadai20260010

A Cross-Domain Survey of Efficient Trustworthy Human-Centric AI

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Abstract

Artificial intelligence (AI) has permeated virtually every sector of modern society, from microelectronic circuit design to urban governance, from creative music generation to clinical decision support. This paper presents a comprehensive cross-domain survey that synthesizes recent advances across four interlocking dimensions: (i) efficient computing architectures, including approximate computing, low-power memory design, and edge inference acceleration; (ii) advanced learning paradigms, encompassing supervised local learning, self-supervised representation learning, spectral convolutional networks, and deep reinforcement learning; (iii) trustworthy AI systems, with emphasis on multimodal forgery detection and explainable localization; and (iv) human-centric applications spanning generative media, intelligent transportation, industrial diagnostics, urban computing, healthcare, education, and recommendation systems. By integrating findings from 41 representative studies, we identify shared methodological threads—efficiency–accuracy trade-offs, alignment with human expectations, robustness under distribution shift, and the centrality of domain-specific inductive biases—and outline open challenges for future research.

Keywords
artificial intelligenceapproximate computingdeep learningtrustworthy AIhuman-centric computingedge inferencemultimodal modelsindustrial diagnosticsurban computing
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Publication details
Journal
Journal of Algorithmic Discovery and Applied AI
Volume
1 (2026)
Issue
1 · Forthcoming issue
Article number
jadai20260010
License
CC BY 4.0