AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The human vector of manipulation is identified as a critical but underexplored dimension of AI safety and a four-layer socio-technical defense framework spanning cognitive, system, institutional, and societal domains is proposed, intended to support comparative analysis, guide empirical evaluation pathways, and inform governance, policy, and system-design decisions across evolving AI deployment environments.
Artificial intelligence (AI) is increasingly embedded in social life, shaping how individuals perceive themselves and others and influencing social dynamics that bear directly on equality and collective welfare. Yet its relationship to social bias is paradoxical: the very systems that can entrench and amplify long-stan...
This paper presents a lifecycle-oriented synthesis of human-AI collaboration risks spanning four stages: task allocation, interaction, feedback, and adoption, and proposes a conceptual interaction model that illustrates how these risks emerge from sociotechnical drivers, interact through cascading pathways, and ultimat...
A Multi-Layer Social-Theoretical AI Ethics Framework (MLST-AEF) that integrates normative ethical reasoning, stakeholder analysis, institutional context, bias and power assessment, and structured decision support is developed.
Human-centered artificial intelligence is usually framed as a design intention: build AI that respects human agency, oversight and wellbeing. This paper argues that intention is not sufficient. AI systems are always deployed inside social conditions — professional hierarchies, institutional rules, time pressure, bias a...
The HCSAIGF contributes to AI governance research by providing an integrated explanatory architecture and offers a conceptual basis for future empirical research and more coherent governance practices.
This study examines the transformative impact of artificial intelligence (AI) on the future of work from a human-centered, interdisciplinary perspective, highlighting the necessity of interdisciplinary collaboration to ensure that AI not only enhances efficiency but also promotes justice, well-being, and sustainability...
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